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# If life was fair would it be boring?

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Does everyone wish for human equality and good global living standards? In what ways would this be a bad thing?

Edited by Voltman
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It would reduce the pleasure of status-superiority and deprive people of the sense of satisfaction they get from knowing that other people have it worse; not to mention the fact that they wouldn't be able to use the fact that other people have it worse to silence people who complain about problems in their own lives.

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I would like everyone to have a good standard of living, few wouldn't. However, I do like the exciting feeling that with a lot of hard work and a bit of good luck, I can make myself ridiculously wealthy. Or if I don't play my cards right, I could end up being an epic failure. It truly is the most dangerous and exciting game. So yes, if life were fair I would be bored.

I think it would be great if we lived really long lives and went through multiple cycles of wealth and poverty. Everyone would have perspective and understanding of the other side. I know several people who started life at the bottom of the working class and through sweat and blood now drive expensive cars and park them at expensive houses. Those people tend to be wise beyond their class.

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I'd say I wish for minimum standards of living. Having everyone be equal would be impossible anyways, people would find some way to improve their status.

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I would like everyone to have a good standard of living, few wouldn't. However, I do like the exciting feeling that with a lot of hard work and a bit of good luck, I can make myself ridiculously wealthy. Or if I don't play my cards right, I could end up being an epic failure. It truly is the most dangerous and exciting game. So yes, if life were fair I would be bored.

If people could actually work their way back up after falling into poverty, that WOULD be fair. It's the fact that once you're poor, you basically have little choice but to take a low-wage job and barely cover your costs month-to-month that prevents most people from going from rags to riches. Yes, you could get lucky and find a niche that pays handsomely and escape poverty that way, but can everyone do this? I believe the answer is no because for everyone to have this kind of social-economic mobility would require it to be equally dangerous for wealth to be lost and I don't think it is that easy to lose if you manage it well. Basically, economy is organized in such a way that prevents people from getting rich (or poor) quick, if at all.

I think it would be great if we lived really long lives and went through multiple cycles of wealth and poverty. Everyone would have perspective and understanding of the other side. I know several people who started life at the bottom of the working class and through sweat and blood now drive expensive cars and park them at expensive houses. Those people tend to be wise beyond their class.

Actually, I think many people who experienced poverty and escaped it avoid contemplating the thought of having to return to such a life. Many would lie, cheat, steal, etc. to maintain their position to avoid going back to what they fear from experience. It would be nice if poverty was regulated in some way that made it manageable so that people would avoid and escape it out of fear and desperation. There's really nothing wrong with living meagerly if you have access to basic means of shelter, warmth, decent nutrition, clean water, hygiene, social-contact, leisure, etc.

Edited by lemur
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If people could actually work their way back up after falling into poverty, that WOULD be fair. It's the fact that once you're poor, you basically have little choice but to take a low-wage job and barely cover your costs month-to-month that prevents most people from going from rags to riches. Yes, you could get lucky and find a niche that pays handsomely and escape poverty that way, but can everyone do this? I believe the answer is no because for everyone to have this kind of social-economic mobility would require it to be equally dangerous for wealth to be lost and I don't think it is that easy to lose if you manage it well. Basically, economy is organized in such a way that prevents people from getting rich (or poor) quick, if at all.

Actually, I think many people who experienced poverty and escaped it avoid contemplating the thought of having to return to such a life. Many would lie, cheat, steal, etc. to maintain their position to avoid going back to what they fear from experience. It would be nice if poverty was regulated in some way that made it manageable so that people would avoid and escape it out of fear and desperation. There's really nothing wrong with living meagerly if you have access to basic means of shelter, warmth, decent nutrition, clean water, hygiene, social-contact, leisure, etc.

I grew up somewhat poor (by American standards). Many months my mother really struggled to keep the lights on. Now I'm in school for chemistry at a major university and I have a 3.9 GPA. I'm actually making money in college through a lot of academic and music scholarship. I plan to get an MBA after that which might hoist me into a delightful income bracket. I went to high school in a school district with one of the lowest graduation rates in the country and statistically a great majority of students didn't attend college. I graduated high school with a 3.8 and a rocking SAT. So one can really "pull themselves up by the bootstraps" and change their financial situation for the better. It takes back breaking work, late nights, sweat, tears, and desire to leave one life behind for a different one. Not trying to pat myself on the back, but I'm saying that life can become whatever one wants if you've got the stones for it. By the way, I've been supporting myself by working the whole time in addition to the scholarship money. My school/work days are about twelve to fourteen hours long but I wouldn't have it any other way. Life is a game. If one wants to win, put on your cleats, get out there and score some goals.

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I grew up somewhat poor (by American standards). Many months my mother really struggled to keep the lights on. Now I'm in school for chemistry at a major university and I have a 3.9 GPA. I'm actually making money in college through a lot of academic and music scholarship. I plan to get an MBA after that which might hoist me into a delightful income bracket. I went to high school in a school district with one of the lowest graduation rates in the country and statistically a great majority of students didn't attend college. I graduated high school with a 3.8 and a rocking SAT. So one can really "pull themselves up by the bootstraps" and change their financial situation for the better. It takes back breaking work, late nights, sweat, tears, and desire to leave one life behind for a different one. Not trying to pat myself on the back, but I'm saying that life can become whatever one wants if you've got the stones for it. By the way, I've been supporting myself by working the whole time in addition to the scholarship money. My school/work days are about twelve to fourteen hours long but I wouldn't have it any other way. Life is a game. If one wants to win, put on your cleats, get out there and score some goals.

I'm sorry but while I'm sincerely happy for you, your story exemplifies the flaws in the ideology of meritocracy. I too, like you, "climbed the ladder of success" from a poor youth to prestigious academic achievement, etc. Only at some point I began to apply the things I was learning to analyze the economic logic of meritocracy in the first place. It's basically a system of artificial rewards for compliant participation in systematic economic structuring whose intent is to disempower individuals by making them specialists dependent on an elaborate division of labor. This system is very sad, imo, because the people who do not get validated as "meriting" "a delightful income bracket" (as you put it) don't have any real means of supporting themselves except by appeasing the gate-keepers of the meritocratic system of income distribution. Access to arable land and the means to utilize it effectively are restricted by a pricing system that requires people to work for money before being able to invest in their own independence. Personally, I don't see why it should be necessary to provide labor to someone else before going to work for yourself. You would probably want to if you had the chance, just to be able to learn skills and techniques of an experienced veteran; but should people really have to indenture themselves for many years to "earn/merit" freedom? What's more, how many people emerge from indenturement (i.e. working for money) to independent homesteading? Most people have gotten so indoctrinated into consumerism that they just put in their 30 years or whatever it comes out to so that they will be able to retire and continue being dependent on the consumer-system, only now without having to put in any labor hours anymore. This system is fine, even good, when you're on the winning end. It's when you get stuck in a dead-end job to pay bills, or even become unemployable, that people should have the option of working for themselves instead of a manager and/or paying clients, imo.

Edited by lemur
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Human equality...oh no...Are you sure you want to remove free bus travel for old people? Or make bus travel free for everyone?

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Say you are playing a sport where the officials are making sure nobody cheats and all play by the same rules, will the game always be boring? The answer is no.

Say we put together a game of ball, where we have a few ringers and many low skilled players, would the game be boring? Or would the game be more interesting if all the players were more tightly bunched with respect to their skill levels?

From the point of view of the players, it gets boring playing against low skilled players or against players who are too good. The first doesn't challenge you or bring out your best, and the second never gives you a chance to practice your skills and improve. If the players are closer, the game can be exciting for all.

The game of life is no different. If life is a battlefield instead of a game, the rules of war are different. There is no such thing as cheating. There is no requirement for a fair fight, since only winning the battle and war matter. But games are more fun and much more relaxing.

Edited by pioneer
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Say you are playing a sport where the officials are making sure nobody cheats and all play by the same rules, will the game always be boring? The answer is no.

Say we put together a game of ball, where we have a few ringers and many low skilled players, would the game be boring? Or would the game be more interesting if all the players were more tightly bunched with respect to their skill levels?

From the point of view of the players, it gets boring playing against low skilled players or against players who are too good. The first doesn't challenge you or bring out your best, and the second never gives you a chance to practice your skills and improve. If the players are closer, the game can be exciting for all.

The game of life is no different. If life is a battlefield instead of a game, the rules of war are different. There is no such thing as cheating. There is no requirement for a fair fight, since only winning the battle and war matter. But games are more fun and much more relaxing.

Good metaphors/analogies, but the real basis of economic life is not game-playing or fighting but rather producing what is needed and wanted. People do fight over "the means of production" as Marx calls it, but labor ultimately comes down to using those means of production to produce what you need and want (and deem morally/ethically good to produce and consume).

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I'm sorry but while I'm sincerely happy for you, your story exemplifies the flaws in the ideology of meritocracy. I too, like you, "climbed the ladder of success" from a poor youth to prestigious academic achievement, etc. Only at some point I began to apply the things I was learning to analyze the economic logic of meritocracy in the first place. It's basically a system of artificial rewards for compliant participation in systematic economic structuring whose intent is to disempower individuals by making them specialists dependent on an elaborate division of labor. This system is very sad, imo, because the people who do not get validated as "meriting" "a delightful income bracket" (as you put it) don't have any real means of supporting themselves except by appeasing the gate-keepers of the meritocratic system of income distribution. Access to arable land and the means to utilize it effectively are restricted by a pricing system that requires people to work for money before being able to invest in their own independence. Personally, I don't see why it should be necessary to provide labor to someone else before going to work for yourself. You would probably want to if you had the chance, just to be able to learn skills and techniques of an experienced veteran; but should people really have to indenture themselves for many years to "earn/merit" freedom? What's more, how many people emerge from indenturement (i.e. working for money) to independent homesteading? Most people have gotten so indoctrinated into consumerism that they just put in their 30 years or whatever it comes out to so that they will be able to retire and continue being dependent on the consumer-system, only now without having to put in any labor hours anymore. This system is fine, even good, when you're on the winning end. It's when you get stuck in a dead-end job to pay bills, or even become unemployable, that people should have the option of working for themselves instead of a manager and/or paying clients, imo.

Whoa, I said I might land a well paid position...who knows these days. I think we can agree on that!

Are you advocating subsistence farming as a viable alternative to a free market meritocracy? Society moved beyond that in the industrial revolution for a reason. People wanted more leisure time, greater access to a variety of goods, and a higher level of general education.

Lemur, you know I've got respect for your skill in philosophy. I agree with your philosophy here, that would be a wonderful world. However, it could never happen if we wish to preserve the quality of life we a have become so accustom to. Technology and the collective human knowledge has advanced to the point where specialization is required. I don't know of anyone who conducts serious research who doesn't spend the majority of their time with books, pencils, computers, and calculators. Do you want a neurosurgeon operating on your brain that doesn't spend all of his time doing neurosurgery?

I can tell you are an advocate of equity over efficiency. This is the great battle of economics. There is no way that the labor force can be divided equitably while still maintaining the level of efficiency that is required to maintain and innovate in our society. A choice must be made between an unfair efficient society and a fair inefficient society. I choose efficiency because I couldn't be writing this post right now if I had to work the farm from sunrise to sunset just to meet my nutritional needs. No one would have time to build computers anyway.

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Whoa, I said I might land a well paid position...who knows these days. I think we can agree on that!

Well-paid is relative. Why would you land a poorly-paid position unless others who do similar work to yours get paid less on average or your field gets decimated and a large proportion of you can't find work doing what you do?

Are you advocating subsistence farming as a viable alternative to a free market meritocracy? Society moved beyond that in the industrial revolution for a reason. People wanted more leisure time, greater access to a variety of goods, and a higher level of general education.

For one, I don't see them as mutually exclusive. The industrial revolution and the technological and labor advances that have evolved from it should not be seen is a definitive economic structure for society but as simply a collection of available technologies and techniques that can be applied in various situations. The leisure time, access to goods, and educational access have all become more constrained due to consumer demand. Many people have to work 40 hours or more, their "free time" is spent engaging in institutionalized consumption or just driving or standing in lines, they have access to a fraction of the goods they desire, and they don't really care about knowledge because they've come to see education as a means to more money and nothing more. I think that the ideology of meritocracy has promoted a belief that things are just the way they are and all you can do is plug into a job and get rewarded for doing your small part to make "the system" keep doing what it does, even if its generating lots of problems and dissatisfaction.

Lemur, you know I've got respect for your skill in philosophy. I agree with your philosophy here, that would be a wonderful world. However, it could never happen if we wish to preserve the quality of life we a have become so accustom to.

This is an assumption that is insistently maintained by a reactionary political stance. Basically what you're saying is "life is good and to keep it nothing should be changed." The problem with that is that things are getting changed all the time for the worse because of the view that there is no change going on as long as people are reacting against change. You could call this reactionary status-quo progressivism.

Technology and the collective human knowledge has advanced to the point where specialization is required. I don't know of anyone who conducts serious research who doesn't spend the majority of their time with books, pencils, computers, and calculators. Do you want a neurosurgeon operating on your brain that doesn't spend all of his time doing neurosurgery?

Specialization and practice certainly benefit skill and efficiency. It just depends on the nature of each particular type of work how much specialization/dedication is needed. A surgeon might perform optimal surgery working, say, 30 hours/week, due to burn-out or other over-working stresses if she does more hours of surgery than that. Why shouldn't that person do other labor during their off-time, though? Instead of the argument you're making, it sounds more like what you want to say is that people who provide a great deal of benefit with their work (such as doctors/surgeons) should be compensated with lavish lifestyles that liberate them from doing their own housework, contributing to their own food supply, etc.

I can tell you are an advocate of equity over efficiency. This is the great battle of economics. There is no way that the labor force can be divided equitably while still maintaining the level of efficiency that is required to maintain and innovate in our society. A choice must be made between an unfair efficient society and a fair inefficient society. I choose efficiency because I couldn't be writing this post right now if I had to work the farm from sunrise to sunset just to meet my nutritional needs. No one would have time to build computers anyway.

No, you're creating a false dichotomy to make equity seem like a necessary cost of efficiency. If you recall, my position wasn't about equality anyway. It was about interdependence and disempowerment through meritocratic economic structuring. It may actually be the case that consolidating various forms of labor by allowing people to develop multiple specialties and avoid monotony of working constantly in one by switching back and forth between more than one, they would actually perform more efficiently. What's more, if you compare different approaches to agriculture: 1) local community farming for produce with consumer-participation 2) local community farming for produce with dedicated specialists 3) distant farming with all production done by dedicated specialists, including transport/distribution; #2 would probably generate the most yield with the least resources, #1 would come in second, and #3 would be the least efficient in terms of resources and, more so, it generates the most free-time which results in an even greater resource-drain as consumers use their free-time to consume more goods and services because they have nothing else constructive to do.

I don't think people would have to work long hours to keep community farms planted, fertilized, mulched, and weeded if it was planned/organized well. I think a little labor could be divided among a large number of people (like jury duty is, for example) and people would spend a weekend a month (like the army reserves) or something similar contributing to local sustainable farming. Anyway, agriculture is just one example since there are many other ways people could become less economically dependent, thus increasing economic efficiency generally, while giving them more of a dense of democratic voice in the overall structuring of their economies than does meritocracy.

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Specialization and practice certainly benefit skill and efficiency. It just depends on the nature of each particular type of work how much specialization/dedication is needed. A surgeon might perform optimal surgery working, say, 30 hours/week, due to burn-out or other over-working stresses if she does more hours of surgery than that. Why shouldn't that person do other labor during their off-time, though? Instead of the argument you're making, it sounds more like what you want to say is that people who provide a great deal of benefit with their work (such as doctors/surgeons) should be compensated with lavish lifestyles that liberate them from doing their own housework, contributing to their own food supply, etc.

Nothing stops them from doing labor on their off time. Then they just made the choice to expend their labor/time capital rather than their currency capital. Every action costs some sort of capital to the individual and to society.

I'm saying that those individuals who posses skills that are of sufficient rarity and value to society will and should be compensated based on the apparent market value of their set of skills. If that compensation is sufficient to allow the employment of outside labor for daily tasks then what is the problem? If someone works forty hours a week and they don't have time to cut their own lawn what is wrong with them paying someone who needs the work to do it for them? Everyone wins in that trade.

It is impossible for society to enjoy the variety of products and services that it does without specialization. Even if one grows a personal vegetable garden, environmental constraints still prevent us from enjoying variety. One just can't grow oranges in Ontario. So we pay orange growing specialists in Florida to do that for us. That is a more efficient use of resources as growing oranges in Florida requires less resources to produce the same amount of oranges. One could say warm climates have a comparative advantage in citrus agriculture.

Also think of opportunity cost. If a doctor earns $100/hr. and can earn$10/hr. sweeping floors, then every hour he sweeps floors cost him $90. Or for every hour he tends to a home garden, lets say he produces$1 of food, he could have bought himself 100x more food than he could have produced himself. Opportunity cost is the reason there will always be an un-equitable division of labor. It is to the advantage of the individual and to the advantage of society as a whole to be devoted specialists.

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Nothing stops them from doing labor on their off time. Then they just made the choice to expend their labor/time capital rather than their currency capital. Every action costs some sort of capital to the individual and to society.

That is very astute analytically, but the challenge is to translate that into a more sophisticated economic analysis.

I'm saying that those individuals who posses skills that are of sufficient rarity and value to society will and should be compensated based on the apparent market value of their set of skills.

People don't get compensated on the basis of "value to society" but on the basis of value to paying customer. Often it is neither rarity nor value that are rewarded but institutionalized need, which may be quite far from any kind of real "need." That's the difference between institutions and reality.

If that compensation is sufficient to allow the employment of outside labor for daily tasks then what is the problem? If someone works forty hours a week and they don't have time to cut their own lawn what is wrong with them paying someone who needs the work to do it for them? Everyone wins in that trade.

That's not what meritocracy is, though. Meritocracy is the belief that people are supposed to be rewarded for their educational or other attainment of institutionalized milestones. Get a certain score on a test, get into a certain college, get certain grades, get the credentials to acquire a certain job, fulfill certain performance criteria and pass assessments, collect salary, benefits, and ultimately pension. The direct relation between worker and production is severed and replaced with institutionalized evaluation criteria. The lawnmower no longer has any relationship to the value of what s/he produces, only the standards of the employer and what the market value of lawn-mowing is. Economic-productivity becomes completely subjective.

It is impossible for society to enjoy the variety of products and services that it does without specialization. Even if one grows a personal vegetable garden, environmental constraints still prevent us from enjoying variety. One just can't grow oranges in Ontario. So we pay orange growing specialists in Florida to do that for us. That is a more efficient use of resources as growing oranges in Florida requires less resources to produce the same amount of oranges. One could say warm climates have a comparative advantage in citrus agriculture.

You're conflating theoretical and practical arguments. You're right that specialization facilitates products and services that wouldn't be available if everyone was a perfect generalist. That, however, doesn't mean that everyone has to be a totally specialized worker for things to function. What's more, it does not mean that the more specialized individuals become, the more beneficial their labor to an overall economy. That's oversimplifying. You have to look at concrete specifics. If you grow locally prolific vegetables in local farms, it reduces the amount of shipping necessary for produce along with a number of other labor-hours needed for longer-distance distribution. Oranges are only more efficiently produced by specialists in orange-groves to the extent that it's not efficient to replace some orange-consumption with consumption of some other locally-grown fruit. Every economic process has multiple levels on which it can be "tweaked" in various ways. It doesn't come down to an either/or question of whether specialism or generalism is ultimately more efficient for ALL economic processes.

Also think of opportunity cost. If a doctor earns $100/hr. and can earn$10/hr. sweeping floors, then every hour he sweeps floors cost him $90. Or for every hour he tends to a home garden, lets say he produces$1 of food, he could have bought himself 100x more food than he could have produced himself. Opportunity cost is the reason there will always be an un-equitable division of labor. It is to the advantage of the individual and to the advantage of society as a whole to be devoted specialists.

Edited by lemur
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While what you say is correct in theory, in practice it turns out not to be. Essential to the OP is the question of what can be interesting. While refighting battles on a boardgame can insulate the intrigue of tactical ingenuity from the actual experience of shooting and being shot at and thus highlight the interesting element, the actual business of fighting battles -- while still retaining that element of interesting intellectual challenge -- is so overwhelmingly an experience of terror and panic that the fact that it could be interesting as well as terrifying is phenomenologically inaccessible to those involved. The same is true of dire poverty. The coping strategies involved can be interesting if you analyze them from afar, but the actual experience is such an existential crisis that the interesting aspect does not appear to those engaged in the struggle.

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While what you say is correct in theory, in practice it turns out not to be. Essential to the OP is the question of what can be interesting. While refighting battles on a boardgame can insulate the intrigue of tactical ingenuity from the actual experience of shooting and being shot at and thus highlight the interesting element, the actual business of fighting battles -- while still retaining that element of interesting intellectual challenge -- is so overwhelmingly an experience of terror and panic that the fact that it could be interesting as well as terrifying is phenomenologically inaccessible to those involved. The same is true of dire poverty. The coping strategies involved can be interesting if you analyze them from afar, but the actual experience is such an existential crisis that the interesting aspect does not appear to those engaged in the struggle.

Basically what you're saying is that transcendence of the suffering involved in both situations is key to the pleasure of engaging in (practical) theorizing. Yet there are progressive levels of immanence that transcend transcendence, so to speak, thereby bringing the theorist ever closer to the experience they are studying. The board-game general, for example, might move on to engage in paint-ball battles or become a general in an actual war. In either case, they would be moving a step closer to actual war from their boardgame, and thus the pleasure of transcending the deepest suffering of war would be intensified by becomiing that much more immersed in the reality of it. The same may be true for a person with the means to transcend poverty to experiment with transcending it by non-monetary means. Sure, if you have the money you can buy your way out of any tight situation, but when you discover a problem-resolution that costs next-to-nothing, you just transcended poverty in a way that is accessible to anyone on a tight budget. If you share your findings with someone else in poverty, it could directly improve their life. That is real, direct power imo.

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It seems that you have moved from the issue of the interest generated by unfair situations to that of the pleasure they can generate, which are two different things.

My basic point would be that while for the oncologist there is great interest, even fascination, in calculating the exact optimum point between killing his patient by the toxic effects of chemotherapy and radiation therapy and their resulting cachexia, the patient dying of cancer -- even if he is also an oncologist -- somehow fails to find this calculation interesting in his own case. You would think that it should logically become more interesting because the cancer-stricken oncologist now has the further motivation that his life is on the line, but in fact the stress of the misfortune seems to extinguish the interest. Existential engagement seems to swamp the capacity of intellectual stimulation to have its usual effects.

This phenomenon is very close to the related question of why films of intellectually engaging and terrifying struggles can be entertaining and interesting, while the same sorts of events in your own life are just terrifying and numbing.

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# Too old?

1. Oct 18, 2007

### terry81

Hi everyone, this is my first post.

I'm after a bit of advice and hopefully this forum may provide me with a consensus.
Over the last couple of years I've become really interested in physics and after toying with the idea for sometime and continually deciding I'm too old, have finally decided to embark on a BSc Physical Science degree with the Open University.

I'm 26 years old and would like to have a career in physics. I do not have any experience, however I'm fully prepared to do the Masters and more once I have completed my degree. I should mention that I am intending to complete my OU degree in less than 4 years by taking almost the maximum allowed credits per year.

Am I too old for this career change to be a serious endeavour?

Regards
Terry

2. Oct 18, 2007

### G01

26 is NOWHERE near too old. I saw a guy graduate in engineering when he was in his 50's. Good Luck to you! Also, remember that there is a difference between a "physics' degree and a "physical science" degree. Make sure you are signing up for the one you want and not the other.

3. Oct 18, 2007

### Winzer

I say to you go for it. You don't wat to be looking back and thiank about what could have been. Also, 26 is not old at all. Are you up to par on your math?

4. Oct 18, 2007

### JasonRox

Yeah, 26 is nothing.

5. Oct 18, 2007

### huckmank

I started my BSEE at 26 and am currently in my 3rd year. Like G01 mentioned, I see guys older than me all over the place.

My best friend since 7th grade's mother went back to school at 45 after he had graduated high school. She went from making $20k a year and hating life to making >$100k and loving what she did.

What a terrible world this would be if 26 was too late to change the direction of your life.

6. Oct 18, 2007

### HallsofIvy

A number of years ago, I had an 86 year old man in one of my classes. He died one week before graduation and they presented his diploma to his widow. Was that "too late"? No, he enjoyed the last years of his life more by going to college.

7. Oct 18, 2007

### kuahji

I started back when I was 23, thought I was too old as I had to start out in intermediate algebra. Glad I stuck with it though, it seems my maturity at the time gave me an edge against other students. Didn't have my parents to pay for everything, so took everything more seriously. Anyway, don't think 26 is too late. There have been people in their 40s in my classes.

8. Oct 18, 2007

Not to get off topic to much, but G01 what is the difference?? I never heard of 'physical' science before

9. Oct 18, 2007

### Dissonance in E

How long does an undergrad eng degree take on average, 4yrs?

10. Oct 18, 2007

BEng Honors is min four years. Of course, you can take longer if thats to much.

11. Oct 18, 2007

### Locrian

Another vote for not too old.

Do take some time and make sure you understand what you'll be doing after the degree though. People's perceptions of physics and the reality can be wildly different.

12. Oct 18, 2007

### Asphodel

Absolutely not. Also, quit toying with it, you'll just get older. ;)

13. Oct 18, 2007

### cristo

Staff Emeritus
Note that the OP is probably from the UK, where bachelors degrees take 3 years full time study.

14. Oct 18, 2007

### Dr. Gonzo

terry81: I enrolled in college at the age of 30, completed an associate's degree, and am now one year away from completing my bachelor's at the age of 35. I'm also considering a 2 year master's program after that. No, 26 is nowhere near too old!

15. Oct 18, 2007

### huckmank

Yeah, in the US at least. Here's my university's four year plan for EE. It can be done in three years if you take two 9 hour summers and one 12 hour summer (governments, english and maybe an elective in the 12 hour summer is not totally unreasonable).

Last edited: Oct 18, 2007
16. Oct 18, 2007

### daveb

I finished by BS in math and physics at the tender age of 42. I'm now 45 and getting my masters in nucear engineering (probably finish next year) and then I may proceed on to one on physics, or straight to phd.

17. Oct 18, 2007

Daveb,
where are you taking your masters?

18. Oct 18, 2007

### symbolipoint

What about your source of income while you are a student? Part time or full time work? What kind? In the field or something else?

19. Oct 19, 2007

### Dissonance in E

thnks guys

20. Oct 19, 2007

### RufusDawes

Ok you might not be too old to study engineering but what about getting your first job. I mean is anyone really going to pick up a 26 or 28 year old graduate when all the others are 22 ?

Anyone gone to school late and gotten a good job offer ?

21. Oct 19, 2007

### bravernix

Why would it matter if the knowledge and ability is the same? This isn't sports. Also, I do not see why one would put their age or birthdate on their resume. How would they even know? In fact, if I was hiring, I think I would actually prefer people a bit older, as it often (not always) can indicate more maturity.

22. Oct 19, 2007

### terry81

Thanks for all the advice guys. Rufus has probably hit the nail on the head for me though. That is the bit that concerns me the most - getting a physics based job after graduating, when I will be in my 30s with no previous hands on experience.

Does anyone know of anybody who has acheived this?

Regards

23. Oct 19, 2007

### RufusDawes

I'm not suggesting anything I am curious. If you want to do it do it. I want to study engineering, I already have a business degree - which I cannot use because I cannot get a job and I don't want to either. The degree was a mistake but I was too pig headed to admit it back then and I made myself finish.

I work in a warehouse for the last year and a half. I decided I want to try and study engineering because I have wanted to do it since I finished highschool but I never did (for various reasons). I've got a lot of retail/customer service experience but no professional experience.

I came here the same as you to find out If anyone older than 25 has reskilled (or skilled in my case) in engineering and been employed without previous experience in the industry.

Theres a guy at my work 36 years old going back to study engineering. There is always the possibility we have not heard of older graduates with no experience getting jobs because they are few in number, not that they are not getting jobs.

My plan is to go through an associates program first. I can pick up some drafting experience while I am at college if I am lucky and tuition is a fraction of the cost. The consequence is that I might have to spend one more year in college if I do not get good grades in the associates degree. The upside is after 2 years I have an exit point and the tuition costs are 10% of the bachelor degree.

I'm justifying this too myself for several reasons, most of them are emotional.

I said if I had studied engineering 5 years ago I would probably have a good job by now. Someone told me that in 5 years time I will be saying the same thing about now.

I have been told that engineering is technique, with hard work you can pick it up.

I'm 24 in 4 years I will be 28. Thats not that old. I should (hopefull) still have a good 20 years of work in me before I really do suffer from age discrimination.

I want to do it - if I feel bad now about not atleast giving it a shot, how will I feel in 10 or 15 years time, when it really is too late.

Make sure you can cut down expenditutes, live at home ? Save lots of money because excessive study can cut down your life time earnings.

24. Oct 20, 2007

### G01

By my understanding and the people I have talked to who have gotten the degree, a "physical science" degree program involves courses in physics and chemistry and they do not take as many upper level courses in either subject. I always thought a good analogy would be the following:

A Physics B.S. is to an Engineering B.S. as a Physical Science B.S. is to an Engineering Technology B.S. Engineering technology is a great field to have a degree in. There is many employment opportunities and good pay, but if you are looking for Engineering, you don't want an Engineering Technology degree. I think the same relationship applies to Physics and Physical Science degrees.

25. Oct 20, 2007

### Astronuc

Staff Emeritus
Actually, I know of a lawyer who decided to go back to school and get an engineering (MS) degree, without a lot of hands-on engineering experience.

It's doable.

When I recommend someone for employment, it is based strictly on the capability regardless of age.

Starting out late, one needs to look ahead to see what areas are most likely to provide a good opportunity for employment - but then that applies to a high school student or undergraduate looking for a major or grad school.

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# genetic algorithm binary encoding

I am trying to write a program for maximizing a function using a genetic algorithm. The function has $n$ integer variables $x_1 \dots x_n$, such that each variable is in the range [-n,n].

What is the most common way to binary-encode integers in the range [-n, n]?

Will it work if, for the initial population, instead of generating numbers between -n and n, I generate numbers from 0 to 2n and encode them in binary and then, while decoding, I subtract n every time for every subsequent generation ?

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@Renan: I am sorry i forgot to mention that I want to use binary encoding. So I am wondering about a good way to binary-encode integers in the range [-n,n]. I have modified the question. –  vjain27 Apr 23 '13 at 1:58
Deleted my comment since it's not relevant, then. –  Renan Apr 23 '13 at 1:58

I think that the GA should converge to giving low fitness values to elements with the wrong sign bit. As you suggested, the problem is somewhat less with two's compliment, as flipping a bit leads to a change of at most $2^{n-1}$ (n is the number of bits), whereas using the sign bit would allow the value to change by $2^n$ if the sign bit was flipped. –  Micah Apr 23 '13 at 2:12
The most common binary representation that I have seen in GA's is grey codes (http://en.wikipedia.org/wiki/Gray_code). In this case, you would represent the numbers as $[0,2n]$, and you would probably have an extra bit in there if $n$ was a power of 2. No matter what, there's no method of binary representation that can span $[-n,n]$ without having some unused representations.


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# dg.differential geometry – Reference request: isomorphic stacks are given by Moro Moro groupoids equivalent of Morita

Leave $$mathcal {G}, mathcal {H}$$ The groupoids are lying. Leave $$B mathcal {G}$$ denotes the main stack $$mathcal {G}$$ packages and $$B mathcal {H}$$ denotes the main stack $$mathcal {H}$$ bunches Then, we have the following result.

The groupoids $$mathcal {G}$$ Y $$mathcal {H}$$ Morita is equivalent if and only if the differentiable batteries $$B mathcal {G}$$ Y $$B mathcal {H}$$ they are isomorphic

The only place where I could find proof of this is (theorem $$2.26$$) the batteries and gerbes of Paper Differences of Kai Behrend and Ping Xu. I understand the test. I think it is not clear (the notion that something is clear depends on the level of experience) and needs some more lines in the test. I have written in detail the proof. It is a long test.

Is there any other place where this result has been tested so that I can quote that document? I have seen three articles that mention this result without any proof and all refer to the article by Kai Behrend and Ping Xu. I'm not asking for proofs. I'm just asking for reference.


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# Photosynthesis

9SCIE - Plants as Producers

2020

## Mahi Tuatahi

Choose two seeds below. In your book, Describe at least two adaptations that help it be dispersed and explain why dispersal is important.

## Ngā Whāinga Ako

1. Describe and explain the process of photosynthesis
2. Explain why plants are so important to life on Earth

## The Importance of Photosynthesis

• The sun is the main source of energy for life on Earth
• Plants are the first step in the energy chain. They convert light into chemical energy (sugar)!
• Next, herbivores consume the plants, and carnivores or omnivores consume the herbivores!

What else do plants do?

• They take in carbon dioxide gas (our waste product)
• They produce oxygen gas for us to breathe!

### The Equation

\begin{aligned} \text{carbon dioxide} + \text{water} \xrightarrow{\text{light}} \text{sugar} + \text{oxygen} + \text{energy} \end{aligned}

## Where Does Photosynthesis Occur?

• Plant cells have 10-100 chloroplasts
• Photosynthesis occurs in chloroplasts
• Chloroplasts have a pigment called chlorophyll which makes them green!

### How do Plants Breathe?

• They have small openings in their leaves called stomata
• These stomata allow carbon dioxide in, and oxygen gas out


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Is there a simpler proof of Van der Waerden's Theorem when there are only two colors?

http://en.wikipedia.org/wiki/Van_der_Waerden%27s_theorem

The usual approach is to induct on the length of the arithmetic progression, which is difficult to simplify directly to the case of two colors.

Does anyone know a different approach?

Thank you.

-

The case for $k$ colours can be done as an easy induction if the $k=2$ case is available to you.

Indeed, suppose we know that VdW is true for $k-1$. Fix a natural number $M$ and a $k$-colouring of the natural numbers. We seek a monochromatic arithmetic progression of length $M$.

First form a $2$-colouring of the natural numbers as follows: we colour $n$ red if $n$ had colour $k$ and we colour $n$ blue if $n$ had one of the other colours $1,\dots k-1$.

Let $N$ be a Van der Waerden number for $k-1, M$. $N$ exists by the inductive hypothesis.

Now we know from the $k=2$ case that either the red numbers or the blue numbers contain an arithmetic progression of length $N$. If the red numbers do, then we win immediately, since that means that there was an arithmetic progression of length $N>>M$ among colour $k$ in our original colouring. So suppose that there is an arithmetic progression among the blue numbers. In other words, there are natural numbers $a,d$ such that every number of the form $a+md$ (where $m=1,\dots,N$) had one of the colours $1,\dots,k-1$.

Now we form a new $k-1$-colouring of the numbers $1,\dots,N$ as follows: give $m$ the colour that $a+md$ had in the original colouring. By what we have just shown, this is a $k-1$-colouring (not a $k$-colouring). Now, by the definition of $N$ there must be a monochromatic arithmetic progression of length $M$; i.e., there are natural numbers $b,f$ such that $b+jf$ has the same colour (in our new colouring of $\{1,\dots,N\}$) for $j=1,\dots,M$.

We use this arithmetic progression to find a monochromatic arithmetic progression of length $M$ in the original colouring. Namely, we have just shown that the numbers $a+(b+jf)d$, for $j=1,\dots,M$ all have the same colour. But we may write

$$a+(b+jf)d=(a+bd) + j(fd)$$

So this is a monochromatic arithmetic progression for the original colouring. $\Box$

Though the proof is not that short, it uses no clever ideas. Roughly, the strategy of the proof can be summarized as follows:

• Work by induction on the number of colours
• Use the $k=2$ case to find a very long arithmetic progression that only takes values among the first $k-1$ colours.
• Apply Van der Waerden's theorm in the $k-1$ case to find a long arithmetic progression inside this arithmetic progression, and use the fact that an arithmetic progression inside an arithmetic progression is still an arithmetic progression.

What does this mean for your question? It means that there is never going to be a proof of the $2$-colour case that is substantially easier than the general case. Roughly speaking, all of the difficult content of the proof is contained in the $2$-colour case; the arbitrary number of colours is not what makes the proof hard; it is the arbitrary length of the arithmetic progression.

So if there were an easier proof of the $2$-colour case, it would automatically translate into an easier proof of the general case; since no such proof is known to exist, the answer to your question is no.

-

There is Shelah's approach for the Hales Jewett theorem (of which Van der Waerden is a special case). See, http://www.math.unh.edu/~dvf/532/Shelah's_proof

Not sure whether it is simpler, though.

-
The link seems to be that. Here is link to a version saved in Wayback machine. – Martin Sleziak Oct 19 at 17:34


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# Hypercube edge-coloring problem

Question: Is there a pairing (a fixed point free involution) of the vertices of the $n$-dimensional cube graph, and a $2$-coloring of its edges such that the number of color changes needed to get from a vertex to its pair is on average more than $c \sqrt{n}$ for a constant $c$?

Motivation: If we chose the involution that takes every vertex to its antipodal pair, and ask for the minimal number of color changes, it is open whether it is $o(n)$, so a proof would answer a question of Leader and Long affirmatively.

Examples: You can achieve the order of magnitude of $\sqrt{n}$ by setting the involution to pair a vertex to its antipodal pair, and coloring the edges between the same levels of the cube with the same color, such that neighbouring levels get different colors. This way the minimum is one (or zero according to parity, attained at the middle level), and the average is $c \sqrt{n}$. One can do "worse" by changing the involution to send the middle layers to the top/bottom of the cube, but this will only make the minimum $c'\sqrt{n}$ and the average also $c'' \sqrt{n}$.

• This seems to speak to colorings more than to involutions. If I color edges so that edge gets color c(i) if the vertices differ in the ith bit, then I can find a path between any two vertices with at most one color change. It might be interesting to classify colorings which are small variations on this which admit "similarly boring" paths between any two points. – The Masked Avenger Mar 6 '15 at 18:42


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# Adnan Bashir Michoacán University, Mexico Michoacán University, Mexico Argonne National Laboratory, USA Kent State University, USA γ * π 0 γ Transition.

## Presentation on theme: "Adnan Bashir Michoacán University, Mexico Michoacán University, Mexico Argonne National Laboratory, USA Kent State University, USA γ * π 0 γ Transition."— Presentation transcript:

Adnan Bashir Michoacán University, Mexico Michoacán University, Mexico Argonne National Laboratory, USA Kent State University, USA γ * π 0 γ Transition Form Fatctor: QCD & BaBar γ * π 0 γ Transition Form Fatctor: QCD & BaBar March 15, 2012 Jefferson Laboratory, USA

Contents Pion Electromagnetic Form Factor Pion Electromagnetic Form Factor Pion Electromagnetic Form Factor Pion Electromagnetic Form Factor Pion Electromagnetic Form Factor Pion Electromagnetic Form Factor Pion Electromagnetic Form Factor Pion Electromagnetic Form Factor Conclusions Conclusions Conclusions Conclusions Conclusions Conclusions Conclusions Conclusions Introduction Introduction Introduction Introduction Introduction Introduction Introduction Introduction Schwinger-Dyson Equations Schwinger-Dyson Equations Schwinger-Dyson Equations Schwinger-Dyson Equations Schwinger-Dyson Equations Schwinger-Dyson Equations Schwinger-Dyson Equations Schwinger-Dyson Equations Quark Propagator: Quark Mass Function Quark Propagator: Quark Mass Function Quark Propagator: Quark Mass Function Quark Propagator: Quark Mass Function Quark-Photon Vertex Quark-Photon Vertex Quark-Photon Vertex Quark-Photon Vertex Pion Bethe-Salpeter Amplitude Pion Bethe-Salpeter Amplitude Pion Bethe-Salpeter Amplitude Pion Bethe-Salpeter Amplitude Pion Transition Form Factor Pion Transition Form Factor Pion Transition Form Factor Pion Transition Form Factor Pion Transition Form Factor Pion Transition Form Factor Pion Transition Form Factor Pion Transition Form Factor

The transition form factor is measured The transition form factor is measured through the process: through the process: The transition form factor is measured The transition form factor is measured through the process: through the process: Introduction

The transition form factor: The transition form factor: The transition form factor: The transition form factor: CELLO CELLO H.J. Behrend et.al., Z. Phys C49 401 (1991). 0.7 – 2.2 GeV 2 CLEO CLEO J. Gronberg et. al., Phys. Rev. D57 33 (1998). 1.7 – 8.0 GeV 2 BaBar BaBar R. Aubert et. al., Phys. Rev. D80 052002 (2009). 4.0 – 40.0 GeV 2 The leading twist pQDC calculation was carried out in: The leading twist pQDC calculation was carried out in: The leading twist pQDC calculation was carried out in: The leading twist pQDC calculation was carried out in: G.P. Lepage, and S.J. Brodsky, Phys. Rev. D22, 2157 (1980). Introduction

Collinear factorization: Collinear factorization: Collinear factorization: Collinear factorization: Transition form factor is the correlator of two currents : Transition form factor is the correlator of two currents : Transition form factor is the correlator of two currents : Transition form factor is the correlator of two currents : T: hard scattering amplitude with quark gluon sub-processes. T: hard scattering amplitude with quark gluon sub-processes. T: hard scattering amplitude with quark gluon sub-processes. T: hard scattering amplitude with quark gluon sub-processes. is the pion distribution amplitude: is the pion distribution amplitude: is the pion distribution amplitude: is the pion distribution amplitude: In asymptotic QCD: In asymptotic QCD: In asymptotic QCD: In asymptotic QCD:

Schwinger-Dyson Equations Schwinger-Dyson Equations Schwinger-Dyson Equations Schwinger-Dyson Equations Schwinger-Dyson Equations (SDE) are the fundamental Schwinger-Dyson Equations (SDE) are the fundamental Schwinger-Dyson Equations (SDE) are the fundamental Schwinger-Dyson Equations (SDE) are the fundamental equations of a field theory. equations of a field theory. Schwinger-Dyson Equations (SDE) are the fundamental Schwinger-Dyson Equations (SDE) are the fundamental Schwinger-Dyson Equations (SDE) are the fundamental Schwinger-Dyson Equations (SDE) are the fundamental equations of a field theory. equations of a field theory. SDE for QCD have been extensively applied to meson SDE for QCD have been extensively applied to meson SDE for QCD have been extensively applied to meson SDE for QCD have been extensively applied to meson spectra and interactions below the masses ~ 1 GeV. spectra and interactions below the masses ~ 1 GeV. SDE for QCD have been extensively applied to meson SDE for QCD have been extensively applied to meson SDE for QCD have been extensively applied to meson SDE for QCD have been extensively applied to meson spectra and interactions below the masses ~ 1 GeV. spectra and interactions below the masses ~ 1 GeV. SDE have been employed to calculate: SDE have been employed to calculate: SDE have been employed to calculate: SDE have been employed to calculate: SDE have been employed to calculate: SDE have been employed to calculate: SDE have been employed to calculate: SDE have been employed to calculate: P. Maris, C.D. Roberts, Phys. Rev. C56 3369 (1997). P. Maris, P.C. Tandy, Phys. Rev. C62 055204 (2000). D. Jarecke, P. Maris, P.C. Tandy, Phys. Rev. C67 035202 (2003). the masses, charge radii and decays of mesons the masses, charge radii and decays of mesons the masses, charge radii and decays of mesons the masses, charge radii and decays of mesons elastic pion and kaon form factors elastic pion and kaon form factors elastic pion and kaon form factors elastic pion and kaon form factors pion and kaon valence quark-distribution functions pion and kaon valence quark-distribution functions pion and kaon valence quark-distribution functions pion and kaon valence quark-distribution functions T. Nguyen, AB, C.D. Roberts, P.C. Tandy, T. Nguyen, AB, C.D. Roberts, P.C. Tandy, Phys. Rev. C83062201 (2011). nucleon form factors nucleon form factors nucleon form factors nucleon form factors G. Eichmann, et. al., G. Eichmann, et. al., Phys. Rev. C79 012202 (2009). D. Wilson, L. Chang and C.D. Roberts, Phys. Rev. C85 025205 (2012). “Collective Perspective on advances in DSE QCD”,AB, L. Chang, I.C. Cloet, B. El Bennich, Y. Liu, C.D. Roberts, P.C. Tandy, arXiv:1201.3366[nucl-th]

Schwinger-Dyson Equations Schwinger-Dyson Equations Schwinger-Dyson Equations Schwinger-Dyson Equations Rainbow-ladder truncation Rainbow-ladder truncation Rainbow-ladder truncation Rainbow-ladder truncation SDE: Full quark propagator: SDE: Full quark propagator: SDE: Full quark propagator: SDE: Full quark propagator:

Schwinger-Dyson Equations Schwinger-Dyson Equations Schwinger-Dyson Equations Schwinger-Dyson Equations Contact interaction: Contact interaction: Contact interaction: Contact interaction:

Bethe-Salpeter amplitude for the pion: Bethe-Salpeter amplitude for the pion: Bethe-Salpeter amplitude for the pion: Bethe-Salpeter amplitude for the pion: Goldberger-Triemann relations: Goldberger-Triemann relations: Schwinger-Dyson Equations Schwinger-Dyson Equations Schwinger-Dyson Equations Schwinger-Dyson Equations

P. Maris and C.D. Roberts, Phys. Rev. C58 3659-3665 (1998). Thus the pseudo-vector Thus the pseudo-vector component of the BS- component of the BS- amplitude dictates the amplitude dictates the transition of the pion transition of the pion form factor to the form factor to the perturbative limit. perturbative limit. Thus the pseudo-vector Thus the pseudo-vector component of the BS- component of the BS- amplitude dictates the amplitude dictates the transition of the pion transition of the pion form factor to the form factor to the perturbative limit. perturbative limit. Pion Form Factor: Pion Form Factor: Pion Form Factor: Pion Form Factor: Schwinger-Dyson Equations Schwinger-Dyson Equations Schwinger-Dyson Equations Schwinger-Dyson Equations

For the contact interaction: For the contact interaction: For the contact interaction: For the contact interaction: Schwinger-Dyson Equations Schwinger-Dyson Equations Schwinger-Dyson Equations Schwinger-Dyson Equations Employing a proper time regularization scheme, one can Employing a proper time regularization scheme, one can ensure (i) confinement, (ii) axial vector Ward ensure (i) confinement, (ii) axial vector Ward Takahashi identity is satisfied and (iii) the corresponding Takahashi identity is satisfied and (iii) the corresponding Goldberger-Triemann relations are obeyed: Goldberger-Triemann relations are obeyed: Employing a proper time regularization scheme, one can Employing a proper time regularization scheme, one can ensure (i) confinement, (ii) axial vector Ward ensure (i) confinement, (ii) axial vector Ward Takahashi identity is satisfied and (iii) the corresponding Takahashi identity is satisfied and (iii) the corresponding Goldberger-Triemann relations are obeyed: Goldberger-Triemann relations are obeyed:

AB, M.R. Pennington Phys. Rev. D50 7679 (1994) AB, M.R. Pennington Phys. Rev. D50 7679 (1994) D.C. Curtis and M.R. Pennington Phys. Rev. D42 4165 (1990) D.C. Curtis and M.R. Pennington Phys. Rev. D42 4165 (1990) A. Kizilersu and M.R. Pennington Phys. Rev. D79 125020 (2009) A. Kizilersu and M.R. Pennington Phys. Rev. D79 125020 (2009) L. Chang, C.D. Roberts, Phys. Rev. Lett. 103 081601 (2009) L. Chang, C.D. Roberts, Phys. Rev. Lett. 103 081601 (2009) AB, C. Calcaneo, L. Gutiérrez, M. Tejeda, Phys. Rev. D83 033003 (2011) AB, C. Calcaneo, L. Gutiérrez, M. Tejeda, Phys. Rev. D83 033003 (2011) AB, R. Bermudez, L. Chang, C.D. Roberts,. AB, R. Bermudez, L. Chang, C.D. Roberts, arXiv:1112.4847 [nucl-th]. Phenomenology Gauge Covariance Lattice Multiplicative Renormalization Perturbation Theory Quark-photon/ quark-gluon vertex Schwinger-Dyson Equations Schwinger-Dyson Equations Schwinger-Dyson Equations Schwinger-Dyson Equations Significantly, this last ansatz contains nontrivial factors associated with those tensors whose appearance is solely driven by dynamical chiral symmetry breaking. It yields gauge independent critical coupling in QED. It also reproduces large anomalous magnetic moment for electrons in the infrared. Quark-photon Vertex Quark-photon Vertex Quark-photon Vertex Quark-photon Vertex

Pion Electromagnetic Form Factor Within the rainbow ladder truncation, the elastic Within the rainbow ladder truncation, the elastic electromagnetic pion form factor: electromagnetic pion form factor: Within the rainbow ladder truncation, the elastic Within the rainbow ladder truncation, the elastic electromagnetic pion form factor: electromagnetic pion form factor: The pattern of chiral symmetry breaking dictates the The pattern of chiral symmetry breaking dictates the momentum dependence of the elastic pion form factor. momentum dependence of the elastic pion form factor. The pattern of chiral symmetry breaking dictates the The pattern of chiral symmetry breaking dictates the momentum dependence of the elastic pion form factor. momentum dependence of the elastic pion form factor. L. Gutiérrez, AB, I.C. Cloet, C.D. Roberts, Phys. Rev. C81 065202 (2010). L. Gutiérrez, AB, I.C. Cloet, C.D. Roberts, Phys. Rev. C81 065202 (2010).

Pion Electromagnetic Form Factor When do we expect perturbation theory to set in? When do we expect perturbation theory to set in? When do we expect perturbation theory to set in? When do we expect perturbation theory to set in? Perturbative Momentum transfer Q is primarily shared equally (Q/2) among quarks as BSA is peaked at zero relative momentum. Momentum transfer Q is primarily shared equally (Q/2) among quarks as BSA is peaked at zero relative momentum. Momentum transfer Q is primarily shared equally (Q/2) among quarks as BSA is peaked at zero relative momentum. Momentum transfer Q is primarily shared equally (Q/2) among quarks as BSA is peaked at zero relative momentum. Jlab 12GeV: 2 { "@context": "http://schema.org", "@type": "ImageObject", "contentUrl": "http://images.slideplayer.com/10/2748183/slides/slide_14.jpg", "name": "Pion Electromagnetic Form Factor When do we expect perturbation theory to set in.", "description": "When do we expect perturbation theory to set in. When do we expect perturbation theory to set in. When do we expect perturbation theory to set in. Perturbative Momentum transfer Q is primarily shared equally (Q/2) among quarks as BSA is peaked at zero relative momentum. Momentum transfer Q is primarily shared equally (Q/2) among quarks as BSA is peaked at zero relative momentum. Momentum transfer Q is primarily shared equally (Q/2) among quarks as BSA is peaked at zero relative momentum. Momentum transfer Q is primarily shared equally (Q/2) among quarks as BSA is peaked at zero relative momentum. Jlab 12GeV: 2

We can dress quark-photon vertex: We can dress quark-photon vertex: We can dress quark-photon vertex: We can dress quark-photon vertex: We can dress quark-photon vertex: We can dress quark-photon vertex: We can dress quark-photon vertex: We can dress quark-photon vertex: H.L.L. Robertes, C.D. Roberts, A. Bashir, L.X. Gutiérrez and P.C. Tandy, H.L.L. Robertes, C.D. Roberts, A. Bashir, L.X. Gutiérrez and P.C. Tandy, Phys. Rev. C82, (065202:1-11) 2010. The corresponding IBS-equation thus yields: The corresponding IBS-equation thus yields: The corresponding IBS-equation thus yields: The corresponding IBS-equation thus yields: The corresponding IBS-equation thus yields: The corresponding IBS-equation thus yields: The corresponding IBS-equation thus yields: The corresponding IBS-equation thus yields: Pion Electromagnetic Form Factor

Dressed quark-photon vertex with ρ-pole on the time- Dressed quark-photon vertex with ρ-pole on the time- Dressed quark-photon vertex with ρ-pole on the time- Dressed quark-photon vertex with ρ-pole on the time- like axis does not alter the asymptotic behaviour of the like axis does not alter the asymptotic behaviour of the form factor for large space-like momenta. form factor for large space-like momenta. Dressed quark-photon vertex with ρ-pole on the time- Dressed quark-photon vertex with ρ-pole on the time- Dressed quark-photon vertex with ρ-pole on the time- Dressed quark-photon vertex with ρ-pole on the time- like axis does not alter the asymptotic behaviour of the like axis does not alter the asymptotic behaviour of the form factor for large space-like momenta. form factor for large space-like momenta. H.L.L. Robertes, C.D. Roberts, A. Bashir, L.X. Gutiérrez and P.C. Tandy, Phys. Rev. C82, (065202:1-11) 2010. Pion Electromagnetic Form factor

Pattern of DCSB & Experimental Signatures

QCD based prediction QCD based prediction through SDE through SDE QCD based prediction QCD based prediction through SDE through SDE H.L.L. Robertes, C.D. Roberts, AB, L.X. Gutiérrez and P.C. Tandy, Phys. Rev. C82, (065202:1-11) 2010. Pattern of DCSB & Experimental Signatures Quark propagator: quark mass function Quark propagator: quark mass function Quark propagator: quark mass function Quark propagator: quark mass function Dressed quark-photon vertex Dressed quark-photon vertex Dressed quark-photon vertex Dressed quark-photon vertex Pion Bethe-Salpeter amplitude Pion Bethe-Salpeter amplitude Pion Bethe-Salpeter amplitude Pion Bethe-Salpeter amplitude PQCD prediction PQCD prediction PQCD prediction PQCD prediction Contact interaction Contact interaction Contact interaction Contact interaction

Pattern of DCSB & Experimental Signatures

Other transition form factors are in accordance with Other transition form factors are in accordance with Other transition form factors are in accordance with Other transition form factors are in accordance with asymptotic QCD: asymptotic QCD: Other transition form factors are in accordance with Other transition form factors are in accordance with Other transition form factors are in accordance with Other transition form factors are in accordance with asymptotic QCD: asymptotic QCD:

Could anything conceivably go astray in the experiment? Could anything conceivably go astray in the experiment? Could anything conceivably go astray in the experiment? Could anything conceivably go astray in the experiment? A possible erroneous way to extract the pion transition A possible erroneous way to extract the pion transition form factor from the data could be the problem of form factor from the data could be the problem of π 0 -π 0 subtraction. π 0 -π 0 subtraction. A possible erroneous way to extract the pion transition A possible erroneous way to extract the pion transition form factor from the data could be the problem of form factor from the data could be the problem of π 0 -π 0 subtraction. π 0 -π 0 subtraction. There is another channel which is the crossed channel of virtual Compton scattering on a pion. Diehl et. al. Phys. Rev. D62 073014 (2000). The misinterpretation of some events (where the second π 0 is not seen) may be different at different Q 2. Pattern of DCSB & Experimental Signatures

Conclusions Dynamical chiral symmetry breaking and the momentum dependence of the quark mass function in QCD have experimental signals which enable us to differentiate its predictions from others. Dynamical chiral symmetry breaking and the momentum dependence of the quark mass function in QCD have experimental signals which enable us to differentiate its predictions from others. Dynamical chiral symmetry breaking and the momentum dependence of the quark mass function in QCD have experimental signals which enable us to differentiate its predictions from others. Dynamical chiral symmetry breaking and the momentum dependence of the quark mass function in QCD have experimental signals which enable us to differentiate its predictions from others. In a fully consistent treatment of pion in SDE QCD (static properties, elastic and transition form factors), the asymptotic limit of the product Q 2 G(Q 2 ), is not exceeded at any finite value of spacelike momentum transfer and is in disagreement with BaBar data for large momenta. In a fully consistent treatment of pion in SDE QCD (static properties, elastic and transition form factors), the asymptotic limit of the product Q 2 G(Q 2 ), is not exceeded at any finite value of spacelike momentum transfer and is in disagreement with BaBar data for large momenta. In a fully consistent treatment of pion in SDE QCD (static properties, elastic and transition form factors), the asymptotic limit of the product Q 2 G(Q 2 ), is not exceeded at any finite value of spacelike momentum transfer and is in disagreement with BaBar data for large momenta. In a fully consistent treatment of pion in SDE QCD (static properties, elastic and transition form factors), the asymptotic limit of the product Q 2 G(Q 2 ), is not exceeded at any finite value of spacelike momentum transfer and is in disagreement with BaBar data for large momenta. A fully consistent treatment of the contact interaction model produces results for pion elastic and transition form factors that are in striking disagreement with experiment. A fully consistent treatment of the contact interaction model produces results for pion elastic and transition form factors that are in striking disagreement with experiment. A fully consistent treatment of the contact interaction model produces results for pion elastic and transition form factors that are in striking disagreement with experiment. A fully consistent treatment of the contact interaction model produces results for pion elastic and transition form factors that are in striking disagreement with experiment.

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# New Page

## Evoptool Description

### Algorithms

Evoptool offers a set of algorithm implementations coming from several paradigms within Evolutionary Computation, in the context of black-box optimization of boolean functions.

• Genetic Algorithms (GAs)
• Simple Genetic Algorithm (SGA). It is the original genetic algorithm with truncation selection, bitflip mutation, and single-point crossover.
• SGA with binary tournament selection. It is a variation of the SGA, and it differs for the selection operator that implements a binary tournament selection. Two individuals are picked up randomly from the population, and their fitness compared: the fittest individual is selected for the new population.
• SGA with uniform bitwise crossover. Another variation of SGA, it maintains the truncation selection and bitflip mutation, but implements a uniform bitwise crossover. This variation operator looks each bit of the two individuals string and swap them with a fixed crossover probability.
• Estimation of Distributions Algorithms (EDAs)
• Univariate EDAs:
• Population Based Incremental Learning (PBIL). It maintains a vector of marginal probabilities which is used to sample a new population at each generation. Then, after a selection of the population is performed, the marginal probabilities are computed again and the vector is updated according to a learning rule.
• Univariate Marginal Distribution Algorithm (UMDA). It differs from PBIL in having no learning rule. It simply generate new individuals using marginal probabilities computed as marginal frequencies of the previous population.
• Compact Genetic Algorithm (cGA). It is a space-efficient variation of PBIL that maintains a probability vector, but rather than generating a whole population, only two individuals are generated, from it and then are subjected to tournament. The probability vector is then updated according to a learning rule which considers both individuals.
• Univariate Distribution of Estimation Using Markov Random Field (Univariate DEUM). It uses an univariate Markov Random Field (MRF) to model the relationship between individuals and their fitness. MRF parameters are estimated through maximum likelihood, then the model is used to sample a new population by employing a Gibbs or Metropolis sampler.
• Bivariate EDAs:
• Mutual Information Maximizing Input Clustering (MIMIC). It adopts a directed conditional chain model for the distribution structure, learned by a greedy heuristic employing Shannon's Information Entropy, in order to minimize the Kullback-Leibler Divergence between the model and the true distribution.
• Combining Optimizers with Mutual Information Trees (COMIT). It is similar to MIMIC, but with a tree-structured Bayesian network rather than a single chain. A maximum weight spanning tree is used to learn the structure, guided by the entropy information principle. Once the structure is defined, it samples the individuals according to defined model, and then it exploits fast search algorithms to further improve the generated individuals. Inside Evoptool there are two implementation: one with an hill-climber fast search, and one with a PBIL based fast search.
• Bivariate Chain DEUM (Bivariate DEUM). It differs from the univariate version only for the structure of the underlying probabilistic model, that takes the form of a bivariate undirected chain.
• Ising Model DEUM (Ising DEUM). It is a DEUM like algorithm specifically designed to solve the Ising Spin Glass problem, by employing a bivariate lattice structure.
• Multivariate EDAs:
• Simple Bayesian Optimization Algorithm (sBOA). It uses a Bayesian network to represent the fitness structure, it employs the Bayesian-Dirichlet metric to measure the network quality, and a greedy algorithm to search the space of possible networks. At each generation, it computes and stores in the network the marginal and conditional probabilities of a selection of the original individuals, and than uses the Bayesian network to generate the new population.

### Benchmarks

Evoptool is designed to support the study of black-box optimization for boolean functions. The fitness function of each benchmark accepts as argument a string of boolean variables and returns a real value. Being performance comparison the main purpose of Evoptool, it already includes several optimization problems with different properties, varying from classical toy problems to more complex ones.

A first set of benchmarks is composed of simple toy problems without any variable dependency, therefore with an underlying univariate structure.

• One Max. A simple problem where the returned value is represented by the count of alleles of the individual taking value one. It has a unique optimal solution and every allele has the same relevance in the individual.

$f(x) = \sum_{i = 1}^{n} x_{i}$

Another group of functions is constituted by deceptive problems, designed to mislead classical Genetic Algorithms. Their structure defines dependencies among variables with different orders (bivariate, trivariate, multivariate).

Last but not least, there are implementation of real-like applications as:

# Old Page

## User Manual

### Compile and Run Evoptool

#### Compile all the project

1. In the main folder ../evoptool/trunk/ give a make cleanall command in order to remove all the binary files and library dependencies, and be sure to compile the entire project in the right way.
2. In the main folder ../evoptool/trunk/ give a make all command in order to compile all the libraries and build the executable file.

#### Compile only some libraries

1. In the folder ../evoptool/trunk/mylib/src/ give a make lib command in order to compile and build library associated with that module. Repeat this operation for each library you want to compile.
2. In the folder ../evoptool/trunk/gui/src give a make exe command in order to build the executable file.

#### Run the tool

1. Go to the ../evoptool/trunk/gui/bin/ folder.
2. Run the software with the ./evoptool command.

### Algorthms

This section have the purpose to show the set of algorithms implemented inside evoptool. These algorithms belong to different classes, and they perfom different operations. In the following lines are presented the theoretical bases for better undestand the use of evoptool.

• Genetic Algorithm (GA). Is a search technique used in computing to find exact or approximate solutions to optimization and search problems. They grow a population of abstract representations (chromosomes or genotype) of candidate solutions (individuals or phenotypes) to an optimization problem evolves toward better solutions. In this tool, solutions are represented as binary strings of 0s and 1s, but other encodings are also possible. The evolution usually starts from a population of randomly generated individuals and happens in several generations (selection, crossover, mutation). The new population is then used in the next iteration of the algorithm. Commonly, the algorithm terminates when either a maximum number of generations has been produced, or a satisfactory fitness level has been reached for the population. Nowaday, there are the following GA implemented inside evoptool:
• SGA (Simple Genetic Algorithm). A selection operator is applied to the population and two random recombinator operators called crossover and mutation are applied to the selected population in order to improve the population. SGA uses a truncation selection with a fixed percentage. The variation operators used are Uniform crossover and Bit-Flip mutation.
• SGA with Binary Tournment.
• SGA with Population Wise Uniform Crossover.
• Estimation Distribution Algorithm (EDA). Is powerful population-based searcher where the variation operations traditionally implemented via crossover and mutation in EAs are replaced by the process of random sampling from a probability distribution. The distribution is modified generation after generation, using information obtained from the fitter individuals in the population (estimation). The objective of these changes in the distribution is to increase the probability of generating individuals with high fitness. In our tool there are several EDA implemented such:
• cGA (Compact Genetic Algorithm). It initialize a vector of probabilities where each component follows a Bernoulli distribution with parameter 0.5. Next, two individuals are generated randomly from this vector of probabilities. After the individuals are evaluated, a competion between then is carried out. The competion is held at level of each of the unidimensional variables, in such a way that if for the ith position the conquiring individual take a value different from the loser, the ith component of the vector of probabilities increases its value.
• PBIL (Population Based Incremental Learning). It maintains unconditional probabilities and no interparameter dependencies are modeled. A vector, P, specifies the probability of generating a 1 in each bit position. Initially, all values in P are set to 0.5. A number of solution vectors are generated by stochastically sampling P; each bitis sampled independently of all the others. The probability vector is then moved towards the generated solution vector with the highest evaluation.
• UMDA (Univariate Marginal Distribution Algorithm). At each generation, it estimate the joint probability distribution model of the selected individual by factorize it as product of indipendent univariate marginal distributions. Each univariate marginal distribution is estimated from marginal frequency.
• MIMIC (Mutual Information Maximizing Input Clustering). It attempts to communicate information about the cost function obtained from one iteration of the search to later iterations of the search directly. There are two main components of MIMIC: first, a randomized optimization algorithm that samples from those regions of the input space most likely to contain the maximum; second, an effective density estimator that can be used to capture a wide variety of structure on the input space, and that build a Markov Chain in order to represents the bivariate structure.
• COMIT (Combining Optimizers with Mutual Information Trees). It gains most of the benefits of modeling the dependencies in the search space at a significantly reduced computational cost. COMIT starts by estimating a probabilistic model of the population, than uses the model to stochastically generate new solutions, it selects the fittest solutions and perfom a local fast-search initialized with these solutions.
• COMIT with HillClimbing as fast-search.
• COMIT with PBIL as fast-search.
• DEUM (Distribution Estimation Using Markov Random Field). DEUM uses MRF approach to estimate and sample the probability distribution using a fitness modelling approach to estimate the MRF parameters. This version of DEUM is an univariate model of probability distribution. This allows to completely eliminate the structure learning task.
• DEUM with probability vector. It uses the estimated parameters of MRF for update the probability vector, generation by generation.
• DEUM with direct sampling from Gibbs distribution. It uses the estimated parameters of MFR for directly calcutate the probability of each bit to be 1 or 0.
• Is-DEUM (Ising model DEUM). It is an evolution from the univariate DEUM to a bivariate version. This algorithm has been defined for solve the Ising Sping Glass problem, and therefore it use a grid structure to model the interactions between each variables of a solution.
• Is-DEUM with a Metropolis sampler.
• Is-DEUM with a Gibbs sampler.
• sBOA (Simple Bayesian Optimization Algorithm). It is a multivariate EDA that uses the Bayesian Dirichlet equivalence metric to measure the goodness of each structure. The search used is a greedy search and it starts in each generation from scratch. In order to reduce the cardinality of the search space the constraint that each node in the Bayesian network as at most k parents is assumed.
• Genetic Estimation Distribution Algorithm (GEDA). This class of Evolute Algorithms is a new proposal class, where genetic variation operations such mutation and crossover are applied to a probabilistic model of the population, rather than directly to an individual of the population. Each generation of a GEDA is composed by estimation of population distribution, variation of the probabilistic model, and sampling from the variated model.
• gcGA.
• Operative Reaserch classical algorithms (OR). It is distinguished by its frequent use to examine an entire management information system, rather than concentrating only on specific elements. An operations researcher faced with a new problem is expected to determine which techniques are most appropriate given the nature of the system, the goals for improvement, and constraints on time and computing power. For this and other reasons, the human element of OR is vital. Like any other tools, OR techniques cannot solve problems by themselves.
• Pseudo Boolean Linear Programming.

### Optimization Problems

An optimization problem is defined by a specified problem space X, an objective functions F , a search space G, a set of search operations A. Specifying the elements of this tuple is the most important prerequisite for any experiment. The problem space X is the space of all possible solutions canditates, in our case is defined by every combinations of a fixed lengh bit string. The objective functions F (fitness) measure the utility of a solution. As convention, we decide that every objective function return a real number between in the [0,100] set, and if nothing else is stated, maximization is assumed. The search space G is the space of elements where the search operations are applied. For evoptool is defined by the individual lenght (size). The search operations A are all the operations used for perform the search, and such we compare several algorthms a time, they are defined by the particular algorthm used.

Inside evoptool, is possible to choose between several different Objective Functions and customize a proper Optimization Problem setting the individual size and choosing a set of Algorithms to compare. The following list contains the Objective Functions implemented for the moment.

• AltBits.
• One Max and One Zero Max. The task in the OneMax problem is to find a binary string of length n consisting of all ones. The search and problem space are both the fixed-length bit strings G = X = Bn. Each gene (bit) has two alleles 0 and 1 which also contribute exactly this value to the total fitness.
• BinVal. It is something like a perverted version of the OneMax problem, with the objective function defined as the real value of the binary string. Since the bit at index i has a higher contribution to the fitness than all other bit at higher indices together, the comparison between two solution candidates x1 and x2 is won by the

lexicographically bigger one. We can expect that the bits with high contribution (high salience) will converge quickly whereas the other genes with lower salience are only pressured by selection when all others have already been fully converged.

• F3d Deceptive.
• F3d Deceptive Bipolar.
• F3d Deceptive Ovelapping.
• Four and Six Peaks. This Objective Function have some global optima wich are isolated, and therefore are difficult to find. It also have some local optima which are easy to get and therefore the search algorithms tend to converge on local optima.
• Trap5. Subjected to maximization based on the Hamming distance to a pre-defined global optimum X' . They build a second, local optimum in form of a hill with a gradient pointing away from the global optimum. This trap is specified with two values, b and r, where b corresponds to the width of the attractive basins (in our case b = 5)and r to their relative importance.
• Sat Lib.
• SumVal.
• Liepins Vose Fully Deceptive.
• SpinGlass. A spin-glass instance in the Ising model is given by n spins that can either point up or down. We consider two dimensional Edwards Anderson (EA) spin glass with +/-1 distribution and periodic boundary conditions. Spins are coupled with coupling strengh J. In an EA spin glass spins are located on the sites of a 2-dimensional lattice with nearest neighbor interactions being present. A set of problem instance for each problem size is provided with its best solution from The Spin Glass Ground State Server at the Institut für Informatik of the Universität zu Köln.

### Graphic User Interface

The graphic user interface of evoptool is quite intuitive. The user can easily customize his own experiment, and visualize the results graph. In the next rows there are shortly described the GUI componets and their use.

Evoptool running on Ubuntu 9.04.
1. Control Buttons. There are three control buttons which can be used to start or stop the execution of an experitment, and to force the update of the graphs.
2. Objective Function Settings. This part of the toolbar allows the customization of the Objective Problem. It is possible to select an objective function type and fix the individual size as number of bits.
3. Average Mode Setting. Pushing this toggled button it is possible to enable Average Test Mode. In this mode, evoptool will run a difined number of algortihms for each selected one in the Algorithm Panel, and it will perform the average of the different executions of the same algorithm. This option is usefull to show the average behavior of searchers.
4. Algorithms Panel. This panel allows to select a set of algorithms to compare in a particular execution. It is subdivided in four parts, in order to classify algorithms for belonging kind. Next, is possible to customize the parameters of each selected algorithm.
5. Result Tabs. This tabs show the result graphics of the a test execution in real time mode. At this time, only the average fitness graph is implemented in evoptool, but new graphs can be easily added in future.
6. Log Monitor. This monitor keep the user informed about the execution status and several others information such warnings or errors.

#### Video Example of Use

In this short video is briefly presented an example of use of evoptool. It starts by configuring the Optimization Problem to solve, next select a set of algortihms to compare and finally is showed the execution of a test.

### Command Line

Evoptool is also powered by a command line interface, so when the GUI is not needed and can be an unusefull weight, it is possible to escape it and run the software just typing a command inside a terminal such bash or dash.

#### Commands

• --help (-h). This command print at video usefull information about evoptool commands and their use.
• --version (-v). This command print at video information about currently version of the software and others informations such Copyright or link to the evoptool web page.
• --nogui [CONF_FILE] (-n). This is the more usefull command, such it allows to run evoptool from command line and to achieve the results as an image graph or, for more complex purposes, as data files. This command need only of one parameter that specify the CONFIGURATION FILE for the execution. The configuration file parameter need to be specified with a proper PATH if, and only if, its position differs from the program executable folder. In the next image is possible to view the output generated from an execution of a test from command line.
Evoptool running from command line

#### Configuration File

In order to run evoptool from command line, is needed a proper configuration file that define all the contraints and settings for that particulat test execution. A default configuration file can be found in the folder ../evoptool/trunk/gui/bin/confi.evoptool, and the user is free to customize it or to create a different file in a different position and specify the entire PATH before run evoptool. The configuration file is quite intuitive, and the default file presents several usuful information about the contents and how to modify it. It is subdivided in three main section, each of them define the behavior of a particular component.

• General Settings. Allows to specify if the execution of a test have to be a normal one (Single Run Mode), or an average execution (Multi Run Mode). In the last case, the parameter N_ALGO define the number of execution of an algorithm on that perform the average.
• Objective Function.Allows to specify which is the Objective Function for this test execution and to define the needed parameters as the individual size or others.
• Algorithms. Allows to define a set of algorithms to run, and to specify their parameters individually.

Some usefull information on how to customize the configuration file are listed in the next rows:

• Comment or uncomment lines with a simbol '#' for customize the configuration.
• Mandatory sections are signaled with a simbol '*', other sections can be deleted.
• Uneeded parameters must be commented!
• Values must be between square braces.
• Order of parameters inside sections is mandatory!
• There must be the end of file delimiter at last line.

## Documentation

Evoptool is a software with the purpose to compare the performance of several different algorithms from the Evolutive family and, for obvious reasons, with some algorithms from the classical Operation Research family. Evoptool is written in C++ for the GNU/Linux platform and it exploit the Gtk libraries (in this case gtkmm libraries) and GNUplot utility. Inside this tool there are several implemented algorithms and some wrapped ones from already existing applications.

Evoptool running on Ubuntu 9.04.

### Software Modules

Evoptool is made up of several different modules (or libraries). This architecture make easy to organize files and better understanding how the application work.

• common - It contains commons classes and ancestors for the algorithm modules and for the optimization function module.
• ga - It contains the implementation of several Genetic Algorithms.
• eda - It contains the implementation of several Estimation Distribution Algorithms.
• geda - It contains the implementation of several gEDAs.
• or - It contains the implementation of some algorithms from the classical Operation Research.
• opt-pbl - It contains the implementation of several objective functions (fitness), that represents different problem instances.
• gui - This module is the main one, it contains all the classes for manage GUI (algorithm decorators). From the other side it implements the multithread mechanism under the GUI, and last but not list it contains the wrapped applications and take care about wrapping.
• misc - It contains general utility classes such rondom seed geerator.

### Hierarchies

This section have the purpose to show some structures internal to evoptool implementation. In particular there are presented the main hierarchies of this software, so, future developers will be facilitated during the development of new componets.

#### Algorithms Hierarchy

Algorithms Hierarchy

Every algorithm implemented inside evoptool inherits from the abstract class Algorithm. This defines every common attributes and functions, and it is an hight level of abstraction for an algorithm. Next, the others abstract classes GA, EDA, gEDA defines and some times implements common attributes and functions for each category of algorithm, in order to generalize more possible their own concepts. Finally every concrete algorithm will be inherits from one of those last abstract class, and will be a complete implementation of a particular kind of searcher.

#### Decorators Hierarchy

Algorithm Decorators Hierarchy

Decorators are a very useufull and versatile objects. They allow to easily integrate a new algorithm in the graphic user interface. The abstract class AlgorithmDecorator define some functons that every concrete decorator have to implement. In addiction, it provides the function addParameter that allows a fast customization of each concrete decorator. Finally, a decorator can be viewed as a Wrapper for that algorithms that aren't directly implemented inside evoptool such sBOA.

#### Objective Functions Hierarchy

Objective Functions Hierarchy

This hierarchy define the structure of Objective Functions. As we can see, it is a quite simple architecture. Every objective functionions inherits from the abstract class ObjectiveFunction, that defines an abstract function f that represents the fitness function. Finally every concrete objective function have to implements this function as specified from the Optimization Problem.

## How To Add New Features

Along the next sections will be presented some procedures to follow in order to add new features and components to evoptool. Those instructions are usefull to avoid common errors or to forget some parts of the implementation.

Adding a new algorithm is really an easy task to do. The next lines will show the steps to follow in order to do it. As convenience, we will refere to a generic NewAlgorithm and it will be an algorithm of the category SomeGenetic.

1. Create files. Create the files NewAlgorithm.h and NewAlgorithm.cpp inside the ../evoptool/trunk/somegenetic/src/ folder.
2. Fill files. Every algorithm must inherits from the abstract class Algorithm, or from one of its abstract or concrete implementation. Such it have to be a concrete class, it must implments every abstract function inherited from its superclass.
3. Create Decorator. See next section.
4. Update Configuration Parser. See proper section.

In the following lines there are showed the steps to follow in order to add a new algorithm decorator to the grafic user interface. Here, we suppose to add a decorator called SomeAlgoDecorator for convenience, but obviously these instructions are valid for every decorator.

1. Create files. Create the files SomeAlgoDecorator.h and SomeAlgoDecorator.cpp inside the ../evoptool/trunk/gui/src/ folder.
2. Fill files. Every decorator must inherits from the abstract class AlgorithmDecorator, and must provides a concrete implementation of the abstract function:
• Solver* newInstance(...)
• MultiSolver* newInstance(...)
• string plotProperties(...)
• void initContent(...)
3. Configure headers. Add the SomeAlgoDecorator.h to AlgorithmDecorators.h (note the 's'!) that is placed in the ../evoptool/trunk/gui/src/ folder. This will avoid to include a long list of decorator headers to the main GUI class.
4. Add decorator to GUI. In order to concretly add the decorator to the GUI, is needed to modify the GUI.cpp class, adding proper code lines to the initAlgorithmDecorators function.

### Update Configuration Parser

In order to add a new algorithm in the command line mode, it is need to modify and update the configuration parser, and to perfom other few task. As previous sections, we want to add a generic SomeAlgorithm to the Configuration Parser.

1. Configure headers. Be sure that the file SomeAlgorithm.h is added to the header Algorithms.h (note the 's'!) that is placed in the ../evoptool/trunk/gui/src/ folder. This will avoid to include a long list of headers.
2. Modify Configuration File. Add a new algorithm section to the end of the configuration file. Be sure to fix for that algorithm a proper free identification number. The section must contains every parameters needed by the algorithm.
3. Update Configuration Parser. This task need to update the function parseAlgorithm of the file ConfParser.cpp placed in the ../evoptool/trunk/gui/src/ folder. Look that, such we can run evoptool in different run modes, there are two different implementation of this function that have a different return object. Remember to update both the implementation!
1. Add a new case block to the switch/case structure. Be sure to use as case label the same number used before in the configuration file.
2. Fill the case block with the code for parse the algorithm parameters. Parse every parameter using the just implemented functions. This will avoid to make errors and to spend a lot of time.

This section will lists all the steps to follow in order to add a new objective function, and so add a new optimization problem, to evoptool. Here, we assume for convenience that the objective function to add is called SomeObjFun.

1. Create files. Create the files SomeObjFun.h and SomeObjFun.cpp inside the ../evoptool/trunk/opt-pbl/src/ folder.
2. Fill files. Every objective function must inherits from the abstract class ObjectiveFunction, or from one of its concrete implementation. It must implements or overwrites the abstract function f (fitness) inherited from its superclass.
3. Configure headers. Add the SomeObjFun.h to ObjectiveFunctions.h (note the 's'!) that is placed in the ../evoptool/trunk/gui/src/ folder. This will avoid to include a long list of headers to the main GUI class.
4. Update Enumeration. Add a new value for the enum GUI::ObjFunctions defined in the GUI header file. The header GUI.h can be found in the ../evoptool/trunk/gui/src/ folder.
5. Update GUI. In order to link the just created fitness function with the grafic user interface it is needed to modify the GUI.cpp file.
• getSelectedObjectiveFunction function. Add the new component to the switch/case block.
• initObjList function. Add the new componet to the Gtk::TreeModel object, so it will be showed in the proper selection menu.
6. Update Configuration Parser. This is a quite simple task to do, just look to the code!