The effect of selection from old populations in genetic algorithms

In this paper a method to increase the optimization ability of genetic algorithms (GAs) is proposed. To promote population diversity, a fraction of the worst individuals of the current population is replaced by individuals from an older population. To experimentally validate the approach we have used a set of well-known benchmark problems of tunable difficulty for GAs, including trap functions and NK landscapes. The obtained results show that the proposed method performs better than standard GAs without elitism for all the studied test problems and better than GAs with elitism for the majority of them.