On hybrid genetic models for hard problems

Abstract

We review some main theoretical results about genetic algorithms. We shall take into account some central open problems related with the combinatorial optimization and neural networks theory. We exhibit experimental evidence suggesting that several crossover techniques are not, by themselves, eilective in solving hard problems ii compared with traditional combinatorial optimization techniques. Eventually, we propose a hybrid approach based on the idea oí' combining the action oí crossover, rotation operators and short deterministic simulations oí noiidc tor minis tic searches that are promising to be eilective for hard problems (according to the polynomial reduction theory).