Diversity as a selection pressure in dynamic environments

Abstract

Evolutionary algorithms (EAs) are widely used to deal with optimization problems in dynamic environments (DE) [3]. When using EAs to solve DE problems, we are usually interested in the algorithm's ability to adapt and recover from the changes. One of the main problems facing an evolutionary method when solving DE problems is the loss of genetic diversity.In this paper, we investigate the use of evolutionary multi-objective optimization methods (EMOs) for single-objective DE problems. For that purpose, we introduce an artificial second objective with the aim to maintain useful diversity in the population. Six different artificial objectives are examined and compared.All the results will be compared against a traditional GA and the random immigrants algorithm[4]. NSGA2 is employed as the evolutionary multi-objective technique.