How to prevent genetic algorithm from converging on local minima?

Question

I am trying to build a 4 x 4 sudoku solver by using the genetic algorithm. I have some issues with values converging to local minima. I am using a ranked approach and removing t

Answer 1

I think the Sudoku is a permutation problem. therefore i suggest you to use random permutation numbers for initializing population and use the crossover method which Compatible to permutation problems.

Answer 2

The reason you see rapid convergence is that your methodology for "mating" is not very good. You are always producing two offspring from "mating" of the top two scoring individuals. Imagine what happens when one of the new offspring is the same as your top individual (by chance, no crossover and no mutation, or at least none that have an effect on the fitness). Once this occurs, the top two individuals are identical which eliminates the effectiveness of crossover.

A more typical approach is to replace EVERY individual on every generation. There are lots of possible variations here, but you might do a random choice of two parents weighted fitness.

Regarding population size: I don't know how hard of a problem sudoku is given your genetic representation and fitness function, but I suggest that you think about millions of individuals, not dozens.

If you are working on really hard problems, genetic algorithms are much more effective when you place your population on a 2-D grid and choosing "parents" for each point in the grid from the nearby individuals. You will get local convergence, but each locality will have converged on different solutions; you get a huge amount of variation produced from the borders between the locally-converged areas of the grid.

Another technique you might think about is running to convergence from random populations many times and store the top individual from each run. After you build up a bunch of different local minima genomes, build a new random population from those top individuals.