I'm writing a GP that I need some advice on for crossover operations. The GP is attempting to find the best solution for a matrix that has hard row constraints and softer column constraints.

For a given solution in the population, the rows contain a random combination of object type ids from a fixed set. The GP is trying to find a solution where, after the rows are laid out, if you tally the id's in each column, the number of each type must fall within a recommended range for that id. I wrote a fitness function that allows me to grade the solution on how close it comes to the columns constraints - 100% being all the columns fall within specs.

Since fitness is tied to columns it seems logical that the crossover operation should grab columns of two parents to create a candidate offspring. Should a multipoint crossover be a better way to go? My concern is a crossover operation, almost certainly, will break the row contraints.

Thanks for any advice.

I'm writing a Genetic Program that I need some advice on for crossover operations. The GP is attempting to find the best solution for a matrix that has hard row constraints and softer column constraints.

For a given solution in the population, the rows contain a random combination of object type ids from a fixed set. The GP is trying to find a solution where, after the rows are laid out, if you tally the id's in each column, the number of each type must fall within a recommended range for that id. I wrote a fitness function that allows me to grade the solution on how close it comes to the columns constraints - 100% being all the columns fall within specs.

Since fitness is tied to columns it seems logical that the crossover operation should grab columns of two parents to create a candidate offspring. Should a multipoint crossover be a better way to go? My concern is a crossover operation, almost certainly, will break the row contraints.

Thanks for any advice.