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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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  • 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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  • 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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  • Novel fitness measure for evolutionary image matching simulation

    - by Nick Johnson
    I'm sure many people have already seen demos of using genetic algorithms to generate an image that matches a sample image. You start off with noise, and gradually it comes to resemble the target image more and more closely, until you have a more-or-less exact duplicate. All of the examples I've seen, however, use a fairly straightforward pixel-by-pixel comparison, resulting in a fairly predictable 'fade in' of the final image. What I'm looking for is something more novel: A fitness measure that comes closer to what we see as 'similar' than the naive approach. I don't have a specific result in mind - I'm just looking for something more 'interesting' than the default. Suggestions?

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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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  • 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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  • How to choose an integer linear programming solver ?

    - by Cassie
    Hi all, I am newbie for integer linear programming. I plan to use a integer linear programming solver to solve my combinatorial optimization problem. I am more familiar with C++/object oriented programming on an IDE. Now I am using NetBeans with Cygwin to write my applications most of time. May I ask if there is an easy use ILP solver for me? Or it depends on the problem I want to solve ? I am trying to do some resources mapping optimization. Please let me know if any further information is required. Thank you very much, Cassie.

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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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  • 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 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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  • 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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  • Creating a "crossover" function for a genetic algorithm to improve network paths

    - by Dave
    Hi, I'm trying to develop a genetic algorithm that will find the most efficient way to connect a given number of nodes at specified locations. All the nodes on the network must be able to connect to the server node and there must be no cycles within the network. It's basically a tree. I have a function that can measure the "fitness" of any given network layout. What's stopping me is that I can't think of a crossover function that would take 2 network structures (parents) and somehow mix them to create offspring that would meet the above conditions. Any ideas? Clarification: The nodes each have a fixed x,y coordiante position. Only the routes between them can be altered.

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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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  • Sparse parameter selection using Genetic Algorithm

    - by bgbg
    Hello, I'm facing a parameter selection problem, which I would like to solve using Genetic Algorithm (GA). I'm supposed to select not more than 4 parameters out of 3000 possible ones. Using the binary chromosome representation seems like a natural choice. The evaluation function punishes too many "selected" attributes and if the number of attributes is acceptable, it then evaluates the selection. The problem is that in these sparse conditions the GA can hardly improve the population. Neither the average fitness cost, nor the fitness of the "worst" individual improves over the generations. All I see is slight (even tiny) improvement in the score of the best individual, which, I suppose, is a result of random sampling. Encoding the problem using indices of the parameters doesn't work either. This is most probably, due to the fact that the chromosomes are directional, while the selection problem isn't (i.e. chromosomes [1, 2, 3, 4]; [4, 3, 2, 1]; [3, 2, 4, 1] etc. are identical) What problem representation would you suggest? P.S If this matters, I use PyEvolve.

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  • Significance of Bresenhams Line of Sight algorithm

    - by GamDroid
    What is the significance of Bresenhams Line of Sight algorithm in chasing and evading in games? As far as i know and implemented this algorithm calulates the straight line between two given points. However while implementing it in game development i stored the points calculated using this algorithm in an array.Then im traversing this array for chasing and evading purpose. This looks to be working good with some angles only.In an pixel based environment/tile based. What if there are some obstacles added in the paths of the two points? then this algorithm will not work right? How well can we use the Bresenhams Line algorithm in game development?

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  • Neural Network Basics

    - by Stat Onetwothree
    I'm a computer science student and for this years project, I need to create and apply a Genetic Algorithm to something. I think Neural Networks would be a good thing to apply it to, but I'm having trouble understanding them. I fully understand the concepts but none of the websites out there really explain the following which is blocking my understanding: How the decision is made for how many nodes there are. What the nodes actually represent and do. What part the weights and bias actually play in classification. Could someone please shed some light on this for me? Also, I'd really appreciate it if you have any similar ideas for what I could apply a GA to. Thanks very much! :)

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  • how to tackle this combinatorial algorithm problem

    - by Andrew Bullock
    I have N people who must each take T exams. Each exam takes "some" time, e.g. 30 min (no such thing as finishing early). Exams must be performed in front of an examiner. I need to schedule each person to take each exam in front of an examiner within an overall time period, using the minimum number of examiners for the minimum amount of time (i.e. no examiners idle) There are the following restrictions: No person can be in 2 places at once each person must take each exam once noone should be examined by the same examiner twice I realise that an optimal solution is probably NP-Complete, and that I'm probably best off using a genetic algorithm to obtain a best estimate (similar to this? http://stackoverflow.com/questions/184195/seating-plan-software-recommendations-does-such-a-beast-even-exist). I'm comfortable with how genetic algorithms work, what i'm struggling with is how to model the problem programatically such that i CAN manipulate the parameters genetically.. If each exam took the same amount of time, then i'd divide the time period up into these lengths, and simply create a matrix of time slots vs examiners and drop the candidates in. However because the times of each test are not necessarily the same, i'm a bit lost on how to approach this. currently im doing this: make a list of all "tests" which need to take place, between every candidate and exam start with as many examiners as there are tests repeatedly loop over all examiners, for each one: find an unscheduled test which is eligible for the examiner (based on the restrictions) continue until all tests that can be scheduled, are if there are any unscheduled tests, increment the number of examiners and start again. i'm looking for better suggestions on how to approach this, as it feels rather crude currently.

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