Statistically optimize a genetic algorithms selection operator

I am familiar with the methods of selection for genetic algorithms such as stochastic universal sampling, roulette wheel, tournament and others. However, I realize that these methods are close to random sampling used in statistics. I would like to know if there are implementation methods which are close to statistical clustering based on some features of individuals contained in the population, without having to first check all i开发者_运维知识库ndividuals for that specific feature before doing the sample. Essentially I would like to reduce the randomness of the other sampling methods while maintaining enough diversity in each population.

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For the genetic algorithm generally, look for niching/crowding strategies. They try to preserve a diverse population by e.g. keeping unique or very diverse solutions and replacing solutions in very densly populated regions instead. This is especially useful in multiobjective optimization where the "solution" is a population of non-dominated individuals.

If you don't do multiobjective optimization and you do not need to maintain a diverse population over the whole run then you could also use the Offspring Selection Genetic Algorithm (OSGA). It is comparing children to its parents and only considering them for the next population if they've surpassed their parents in quality. This has been shown to a) work even with unbiased random parent selection and b) maintains the diversity until very late in the search at which point the population converges to a single solution.

You can for example use our software HeuristicLab, try different configurations of genetic algorithms and analyze their behavior. The software is GPL and runs on Windows.