Evolutionary Computation Group

Abstract

Abstract In this paper, we present a genetic algorithm with a very small population and a reinitialization process (a mi-cro genetic algorithm) for solving multiobjective optimization problems. Our approach uses three forms of elitism, includ-ing an external memory (or secondary population) to keep the nondominated solutions found along the evolutionary pro-cess. We validate our proposal using several engineering op-timization problems taken from the specialized literature, and we compare our results with respect to two other algorithms (the NSGA-II and PAES) using three different metrics. Our results indicate that our approach is very efficient (computa