Multi-Objective Optimization Using Multi Parent Crossover Operators

The crossover operator has always been regarded as the primary search operator in Genetic Algorithm (GA) because it exploits the available information from the population about the search space. Moreover, it is one of the components to consider for improving the behavior of the GA. To improve performance of GA multi parent crossover operators have been used. Multi parent crossover operators involve sampling of features of more than two parent solution into the offspring that accelerated speed of convergence to global optima.