How do you avoid an invalid search space in a genetic algorithm?

Question

I am developing a GA for a school project and I've noticed that upon evaluating my functions for fitness, an individual is equivalent to its inverse.

For example, the set (1, 1, -1, 1) is equivalent to (-1, -1, 1, -1). To shrink my search space and reach a solution more efficiently, how can I avoid my crossovers from searching in this second half of the search space?

Answer 1

Try to re-write your fitness function to assign weights appropriately to individuals - not sure on the problem you're trying to solve or how weight can be re-assigned. Remember, fittest individuals should be in the pool and more likely to be selected for crossover - you don't want a pool full of dummys that have the same fitness as good solutions that defeats the purpose of the genetic algorithm

Answer 2

Cut the space in half. Require the first element to be non-negative, then if you have (x_1, .. , x_n) and its inverse (-x_1, .. , -x_n) only one will be in the search space. (if x_1 = 0 they are the same) BTW, what is the problem you are solving?

Answer 3

Its computationally expensive, but something like this should do the job. Ignoring the 1st generation, for every generation >=2:

If the children produced via crossover/mutation is an inverse, ignore it and generate another child till it is a "good" child.