Simulated Crossover in Genetic Algorithms

Abstract A new class of crossover operator, simulated crossover, is presented. Simulated crossover treats the population of a genetic algorithm as a conditional variable to a probability density function that predicts the likelihood of generating samples in the problem space. A specific version of simulated crossover, bit-based simulated crossover, is explored. Its ability to perform schema recombination and reproduction is compared analytically against other crossover operators, and its performance is checked on several test problems.