There are two closely related techniques in genetic programming. One of them is grammar based genetic programming (GBGP), which uses context free grammar to generate a derivative tree which represents the program. The other is grammatical evolution which uses something called genomes and codons, which is then mapped to a phenotype, a realization. The part where I get confused is, the phenotype realization can also be represented as a derivative tree. The codon to rule mapping is done through a …

I am using a genetic algorithm to maximize a few hundred thousand real-valued variables. Each of the variables, $x_i$, has its own independent boundary condition. The fitness function uses each of these variables to compute another value and returns the sum of everything: $$fitness = g(x_1) + g(x_2) + g(x_3) \ + \ ...$$ This is taking incredibly long in python. In this situation, what do I gain by maximizing all values at the same time, i.e. using the genetic …

I am trying to understand Genetic Programming (GP) but I cannot think of any context where GP can be chosen over training Artificial Neural Networks with genetic algorithms. What problems each of them is suitable for? In what context we choose one over the other (if it is not personal preference)?

I am currently writing a program that would be able to play snake on an 25*25 grid. It works by optimizing a set of weights of 300 different solutions (each solution would be a different neural network) with the aid of an evolutionary strategy, thus by random mutation and parent selection. I have decided not to apply crossover on the parents pool due to the black box problem concerning MLPs and other neural networks. My population size is 300 and …

I know that I can use Keras to solve classification problems but is it possible to get it to output a float between 0 and 1000 for instance? I want train my network to approximate the simulation that gives my GA a fitness score because the simulation takes a long time to run.

I've been working with GPlearn for the passed couple of months as part of my research, but it turns out that it provides only a binary symbolic classifier. Do you guys know any other python library which do provide non-binary symbolic classifier and is scikit-learn based API? Thanks!

Assuming all of the following; I have 4 known numbers, all within a 0-400 range, like this: Variable1 Variable2 Variable3 Variable4 0-400 0-400 0-400 0-400 I know that there is a mathematical relationship between the numbers. I would like to use a genetic algorithm (computer code) to estimate/approximiate Variable2 and Variable3 based on Variable1 and Variable4. Also, importantly, assume that there are many input samples and that each sample will differ slightly. Thus, a genetic algorithm optimization of "a mathematical …

I'm working on a optimization problem and using GA algorithm (in MATLAB, ga function). As you know MATLAB plots GA result with two curves, one for the best values and other to show the mean values and when this two curves touch each others it means algorithm has been converged. On my cause these two curves don't get along and continue as two parallel lines until the end of max Generations number and finally a premature convergence,What makes it happen? …

sometimes I see the kernel with GP programming. but without explanation, they put some random number with unknown equation Here is some part of GP programming gp <-function(data) { p<-0.200000*tanh(((((31.006277) * ((((((data$Embarked) + (data$Cabin))/2.0)) + (((((sin((tanh((data$Parch))))) * (3.0))) - (data$Pclass))))))) * 2.0)) + 0.200000*tanh(((31.006277) * (((((data$Age) * (data$Cabin))) + (((((0.318310) - (data$Pclass))) + (pmin((2.0), (((data$Parch) * 2.0)))))))))) + 0.200000*tanh(ifelse(rep(1.,NROW(data))*(ifelse(rep(1.,NROW(data))*(data$SibSp>0.),data$Cabin,sin((data$Parch)))>0.),(7.90205097198486328),(((((((data$Cabin) + (data$Fare))/2.0)) - (9.869604))) - (31.006277)))) + 0.200000*tanh(((((((((((tanh(((((0.636620) < (data$Parch))*1.)))) * 2.0)) - (data$Pclass))) + (((data$Embarked) + (sin((data$Pclass))))))) * 2.0)) * …