A multi-objective relative clustering genetic algorithm with adaptive local/global search based on genetic relatedness

Abstract

This paper describes a new evolutionary algorithm for multiobjective optimization, namely Multi-Objective Relative Clustering Genetic Algorithm (MO-RCGA), inspired by concepts borrowed from gene relatedness and kin selection theory. The proposed algorithm clusters the population into different families based on individual kinship, and adaptively chooses suitable individuals for reproduction. The idea is to use the information on the position of the individuals in the search space provided by such clustering schema to enhance the convergence rate of the algorithm, as well as improve its exploration. The proposed algorithm is tested on ten unconstrained benchmark functions proposed for the special session and competition on multi-objective optimizers held at IEEE CEC 2009. The Inverted Generational Distance (IGD) is used to assess the performance of the proposed algorithm, in comparison with the IGD obtained by state-of-the-art algorithms on the same benchmark.