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

it mainly depends on the search space and the type of crossover you are using. For some problems I found that using crossover at the beginning and then mutation, it will speed up the process for finding a solution, however this is not very good approach since I will end up on finding similar solutions. If we use both crossover and mutation I usually get better optimized solutions. However for some problems crossover can be very destructive.

Also genetic operators alone are not enough to solve large/complex problems. When your operators don't improve your solution (so when they don't increase the value of fitness), you should start considering other solutions such as incremental evolution, etc..