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  • Genetic programming in c++, library suggestions?

    - by shuttle87
    I'm looking to add some genetic algorithms to an Operations research project I have been involved in. Currently we have a program that aids in optimizing some scheduling and we want to add in some heuristics in the form of genetic algorithms. Are there any good libraries for generic genetic programming/algorithms in c++? Or would you recommend I just code my own? I should add that while I am not new to c++ I am fairly new to doing this sort of mathematical optimization work in c++ as the group I worked with previously had tended to use a proprietary optimization package. We have a fitness function that is fairly computationally intensive to evaluate and we have a cluster to run this on so parallelized code is highly desirable. So is c++ a good language for this? If not please recommend some other ones as I am willing to learn another language if it makes life easier. thanks!

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  • Genetic Programming in C#

    - by Mac
    I've been looking for some good genetic programming examples for C#. Anyone knows of good online/book resources? Wonder if there is a C# library out there for Evolutionary/Genetic programming?

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  • How to implement a genetic algorithm with distance, time, and cost

    - by ari
    I want to make a solution to find the optimum route of school visit. For example, I want to visit 5 schools (A, B, C, D, E) in my city. Then I must find out what school I should visit first, then the second, then the third etc. with distance, time, and cost criteria. The problem is, I am confused about how to use distance with time and cost (fuel usage) estimation in genetic algorithm to find the optimum route?

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  • Genetic Programming Online Learning

    - by Lirik
    Has anybody seen a GP implemented with online learning rather than the standard offline learning? I've done some stuff with genetic programs and I simply can't figure out what would be a good way to make the learning process online. Please let me know if you have any ideas, seen any implementations, or have any references that I can look at.

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  • Genetic Algorithm new generation exponentially increasing

    - by Rdz
    I'm programming Genetic Algorithm in C++ and after searching all kind of ways of doing GA'a operators (selection, crossover, mutation) I came up with a doubt. Let's say I have an initial population of 500. My selection will consist in getting the top 20% of 500(based on best fitness). So I get 100 individuals to mate. When I do the crossover I'll get 2 children where both together have 50% of surviving. So far so good. I start the mutation, and everything's ok.. Now when I start choosing the Next generation, I see that I have a big number of children (in this case, 4950 if you wanna know). Now the thing is, every time I run GA, if I send all the children to the next generation, the number of individuals per generation will increase exponentially. So there must be a way of choosing the children to fulfill a new generation without getting out of this range of the initial population. What I'm asking here is if there is anyway of choosing the children to fill the new generations OR should I choose somehow (and maybe reduce) the parents to mate so I don't get so many children in the end. Thanks :)

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  • Genetic Algorithms applied to Curve Fitting

    - by devoured elysium
    Let's imagine I have an unknown function that I want to approximate via Genetic Algorithms. For this case, I'll assume it is y = 2x. I'd have a DNA composed of 5 elements, one y for each x, from x = 0 to x = 4, in which, after a lot of trials and computation and I'd arrive near something of the form: best_adn = [ 0, 2, 4, 6, 8 ] Keep in mind I don't know beforehand if it is a linear function, a polynomial or something way more ugly, Also, my goal is not to infer from the best_adn what is the type of function, I just want those points, so I can use them later. This was just an example problem. In my case, instead of having only 5 points in the DNA, I have something like 50 or 100. What is the best approach with GA to find the best set of points? Generating a population of 100, discard the worse 20% Recombine the remaining 80%? How? Cutting them at a random point and then putting together the first part of ADN of the father with the second part of ADN of the mother? Mutation, how should I define in this kind of problem mutation? Is it worth using Elitism? Any other simple idea worth using around? Thanks

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  • Implementing crossover in genetic programming

    - by Name
    Hi, I'm writing a genetic programming (GP) system (in C but that's a minor detail). I've read a lot of the literature (Koza, Poli, Langdon, Banzhaf, Brameier, et al) but there are some implementation details I've never seen explained. For example: I'm using a steady state population rather than a generational approach, primarily to use all of the computer's memory rather than reserve half for the interim population. Q1. In GP, as opposed to GA, when you perform crossover you select two parents but do you create one child or two, or is that a free choice you have? Q2. In steady state GP, as opposed to a generational system, what members of the population do the children created by crossover replace? This is what I haven't seen discussed. Is it the two parents, or is it two other, randomly-selected members? I can understand if it's the latter, and that you might use negative tournament selection to choose members to replace, but would that not create premature convergence? (After a crossover event the population contains the two original parents plus two children of those parents, and two other random members get removed. Elitism is inherent.) Q3. Is there a Web forum or mailing list focused on GP? Oddly I haven't found one. Yahoo's GP group is used almost exclusively for announcements, the Poli/Langdon Field Guide forum is almost silent, and GP discussions on general/game programming sites like gamedev.net are very basic. Thanks for any help you can provide!

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  • Genetic algorithms with large chromosomes

    - by Howie
    I'm trying to solve the graph partitioning problem on large graphs (between a billion and trillion elements) using GA. The problem is that even one chromosome will take several gigs of memory. Are there any general compression techniques for chromosome encoding? Or should I look into distributed GA? NOTE: using some sort of evolutionary algorithm for this problem is a must! GA seems to be the best fit (although not for such large chromosomes). EDIT: I'm looking for state-of-the-art methods that other authors have used to solved the problem of large chromosomes. Note that I'm looking for either a more general solution or a solution particular to graph partitioning. Basically I'm looking for related works, as I, too, am attempting using GA for the problem of graph partitioning. So far I haven't found anyone that might have this problem of large chromosomes nor has tried to solve it.

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  • Initial Genetic Programming Parameters

    - by cmptrer
    I did a little GP (note:very little) work in college and have been playing around with it recently. My question is in regards to the intial run settings (population size, number of generations, min/max depth of trees, min/max depth of initial trees, percentages to use for different reproduction operations, etc.). What is the normal practice for setting these parameters? What papers/sites do people use as a good guide?

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  • cool project to use a genetic algorithm for?

    - by Ryan
    I'm looking for a practical application to use a genetic algorithm for. Some things that have thought of are: Website interface optimization Vehicle optimization with a physics simulator Genetic programming Automatic test case generation But none have really popped out at me. So if you had some free time (a few months) to spend on a genetic algorithms project, what would you choose to tackle?

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  • What problems have you solved using genetic algorithms/genetic programming?

    - by knorv
    Genetic algorithms (GA) and genetic programming (GP) are interesting areas of research. I'd like to know about specific problems you - the SO reader - have solved using GA/GP and what libraries/frameworks you used if you didn't roll your own. Questions: What problems have you used GA/GP to solve? What libraries/frameworks did you use? I'm looking for first-hand experiences, so please do not answer unless you have that.

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  • Traveling Salesman - Nearest Neighbor vs Genetic DEATHMATCH

    - by EvilTeach
    Over the last few days I have noted a few web sites that demonstrated TS solution using genetic algorithms. I am looking for your opinion which is better for this particular problem. Heuristics vs Genetic. By better, I mean will yield a shorter/lower cost path. Explain why you feel the way that you do. Examples, and off-site links are welcome.

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  • Genetic algorithm resource

    - by Siblja
    Lately I'm interested in a topic of genetic algorithms, but i couldn't find any good resource. If you know any good resource, book or a site i would appreciate it. I have solid knowledge of algorithms and A.I. but im looking for something with good introduction in genetic programing.

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  • Tracking fitness in a genetic algorithm

    - by Chuck Vose
    I'm still hacking on my old ruby for the undead post (I know, I know, stop trying to bring the post back from the dead Chuck). But the code has gotten a little out of hand and now I'm working on a genetic algorithm to create the ultimate battle of living and dead with the fitness being how long the battle lasts. So, I've got the basics of it down; how to adjust attributes of the game and how to acquire the fitness of a solution, what I can't figure out is how to store the fitness so that I know when I've tried a combination before. I've not been able to find much genetic code to look at let alone code that I can read well enough to tell what's going on. Does anyone have an idea how this is normally done or just simply an algorithm that could help point me in the right direction?

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  • How should I Test a Genetic Algorithm

    - by James Brooks
    I have made a quite few genetic algorithms; they work (they find a reasonable solution quickly). But I have now discovered TDD. Is there a way to write a genetic algorithm (which relies heavily on random numbers) in a TDD way? To pose the question more generally, How do you test a non-deterministic method/function. Here is what I have thought of: Use a specific seed. Which wont help if I make a mistake in the code in the first place but will help finding bugs when refactoring. Use a known list of numbers. Similar to the above but I could follow the code through by hand (which would be very tedious). Use a constant number. At least I know what to expect. It would be good to ensure that a dice always reads 6 when RandomFloat(0,1) always returns 1. Try to move as much of the non-deterministic code out of the GA as possible. which seems silly as that is the core of it's purpose. Links to very good books on testing would be appreciated too.

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  • Is the way I'm implementing my genetic algorithm right?

    - by Mhjr
    In my graduation project, I am asked to use a genetic algorithm (any variation of it can be chosen) to generate valid timetables. What I did was make a simple program that generates unique sequences representing genes, the sequence is described below: (sorry if it's mathematically incorrect) The only variable in the sequence is the room element, so basically the program takes a tree that goes like this: [Course] -(contains)-> [Units] -(contains)-> [Offerings] -(contains)-> [Instructors] -(contains)-> [Rooms] Each course can have n units (duplicates). Each unit can have n offerings (lectures,lab session, excercises,...). Each offering has only 1 instructor. Each instructor (or the whole lecture composed from the four elements of the sequence) has multiple rooms. When a timetable is initialized, one of these sequences that differ in rooms will be taken into the timetable, so the difference in genes (sequences) of each timetable will be just the rooms random choice and the difference between chromosomes (timetables) will be time placements of these genes (sequences). My question is, before I proceed in implementing what I described, is it valid? Is the representation used here for chromosomes a permutation representation?

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  • Neural Net Optimize w/ Genetic Algorithm

    - by ServAce85
    Is a genetic algorithm the most efficient way to optimize the number of hidden nodes and the amount of training done on an artificial neural network? I am coding neural networks using the NNToolbox in Matlab. I am open to any other suggestions of optimization techniques, but I'm most familiar with GA's.

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