A multi-operator self-tuning genetic algorithm for optimization

We propose a multi operator self-tuning GA (MSGA) for the optimization problem. The MSGA is designed with multiple operators-single-point crossover (CR), single-point mutation (MU), single-point copy (CO) and single-point exchange (EX). The MU, CO and EX operators are considered as a group of mutation. The other group is a simple crossover operator. These two groups of operators will do the search job. In a mutation loop, fuzzy reasoning is applied to adjust the population size of each mutation operator effectively.