Efficiency of crossover in genetic algorithms

Question

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 f

Answer 1

The crossover and mutation!! Actually both of them are necessary. Crossover is an explorative operator, but the mutation is an exploitive one. Considering the structure of solutions, problem, and the likelihood of optimization rate, its very important to select a correct value for Pc and Pm (probability of crossover and mutation).

Check this GA-TSP-Solver, it uses many crossover and mutation methods. You can test any crossover alongside mutations with given probabilities.