A multi-sexual genetic algorithm for multiobjective optimization

Abstract

In this paper a new method for solving multicriteria optimization problems by Genetic Algorithms is proposed. Standard Genetic Algorithms use a population, where each individual has the same sex (or has no sex) and any two individuals can be crossed over. In the proposed Multisexual Genetic Algorithm (MSGA), individuals have an additional feature, their sex or gender and one individual from each sex is used in the recombination process. In our multicriteria optimization application there are as many sexes as optimization criteria and each individual is evaluated according to the optimization criterion related to its sex. Furthermore, a multi-parent crossover is applied to generate offspring of parents belonging to all different sexes, so the offspring represents intermediate solutions not totally optimal with respect to any single criterion. During the execution of the algorithm the set of nondominated solutions is updated and this set is presented as the output of MSGA at the end.