genetic-algorithm

I'm doing a final year project on genetic algorithms - specifically of the Dawkins Weasel type. I've done roulette selection and tournament selection, still to do steady state selection, but I'm not sure exactly what it is and references I find online are all pretty vague. Does anyone know how it should be implemented? Any pointers would be great. Many thanks. Typically, the run of a genetic algorithm is divided into generations - each generation your selection and reproduction process replaces all (or at least most) of the population. In a steady state genetic algorithm you only replace a few

I've implemented a number of genetic algorithms to solve a variety of a problems. However I'm still skeptical of the usefulness of crossover/recombination. I usually first implement mutation before implementing crossover. And after I implement crossover, I don't typically see a significant speed-up in the rate at which a good candidate solution is generated compared to simply using mutation and introducing a few random individuals in each generation to ensure genetic . Of course, this may be attributed to poor choices of the crossover function and/or the probabilities, but I'd like to get some

I'd like to optimize three parameters(gamma, cost and epsilon) in eps-regression(SVR) using GA in R. Here's what I've done. library(e1071) data(Ozone, package="mlbench") a<-na.omit(Ozone) index<-sample(1:nrow(a), trunc(nrow(a)/3)) trainset<-a[index,] testset<-a[-index,] model<-svm(V4 ~ .,data=trainset, cost=0.1, gamma=0.1, epsilon=0.1, type="eps-regression", kernel="radial") error<-model$residuals rmse <- function(error) #root mean sqaured error { sqrt(mean(error^2)) } rmse(error) Here, I set cost,gamma and epsilon to be 0.1 respectively, but I don't think they are the best value. So, I'd like

I have always loved the idea of AI and evolutionary algorithms. Unfortunately, as we all know, the field hasn't developed nearly as fast as expected in the early days. What I am looking for are some examples that have the "wow" factor: Self-directed learning systems that adapted in unexpected ways. Game agents that were particularly dynamic and produced unexpected strategies Symbolic representation systems that actually produced some meaningful and insightful output Interesting emergent behavior in multiple agent systems. Let's not get into the semantics of what defines AI. If it looks or

I used the JGAp java genetic algorithm library. and when I evaluated the chromosomes , I had duplication of chromosomes in the population sample run : evaluation 0 A B C A D F S F W evaluation 1: A B C A D F A D F evaluation 2: A D F A D F A D F and the configurations I have used are: conf.setKeepPopulationSizeConstant(true); conf.setRandomGenerator(new StockRandomGenerator()); conf.verifyStateIsValid(); is there any configuration that I can used in order to get unique chromosomes? You can make sure only to use NaturalSelectors that does not allow duplicates. Example code: conf

I need to solve simultaneous linear equations (5 equations with 7 unknowns i.e an under-determined problem) where the variables vary over a wide range of [0 - 1,00,000]. Can someone suggest what fitness function I should use? I guess you are referring to a system of 5 linear equations with 7 variables. This paper seems to show what you're looking for. You basically need to define a cost function and use the GA to minimize it. Search the pdf for "fitness function" to see exactly how to do this. The idea is to find some measure of how well your set of variable approximates the solution (or a

I came across this interesting video on YouTube on genetic algorithms . As you can see in the video, the bots learn to fight. Now, I have been studying neural networks for a while and I wanted to start learning genetic algorithms.. This somehow combines both. How do you combine genetic algorithms and neural networks to do this? And also how does one know the error in this case which you use to back-propagate and update your weights and train the net? And also how do you think the program in the video calculated its fitness function ? I guess mutation is definitely happening in the program in