On hybrid genetic models for hard problems

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 if compared with traditional combinatorial optimization techniques. Eventually, we propose a hybrid approach based on the idea of combining the action of crossover, rotation operators and short deterministic simulations of nondeterministic searches that are promising to be eilective for hard problems (according to the polynomial reduction theory).