Title: Archived elitism in evolutionary computation: towards improving solution quality and population diversity
Authors: Maxim A. Dulebenets
Addresses: Department of Civil and Environmental Engineering, Florida A&M University-Florida State University College of Engineering, 2525 Pottsdamer Street, Building A, Suite A124, Tallahassee, FL 32310-6046, USA
Abstract: Many evolutionary algorithms, developed for solving complex optimisation problems, deploy the elitist strategy. The elitist strategy ensures that a group of the fittest individuals will be transferred to the next generation before performing any algorithmic operations. In general, elitism allows improving the algorithmic performance in terms of solution quality. However, transferring a group of the fittest individuals to the next generation will increase the selection pressure and significantly limit chances of the newly created offspring chromosomes to survive. In order to address the latter drawbacks, this study proposes and evaluates a number of alternative archive-based elitist strategies, where the fittest individuals are stored in the archive and transferred from that archive into the population based on certain rules. The computational experiments are conducted for the unrelated machine scheduling problem, where the total job processing cost is minimised. The results indicate that the proposed 'strong archived elitism' strategy, which samples the best individual discovered from the archive in every generation, outperforms the other elitist strategies in terms of the objective function values by up to 8.29% over the considered problem instances. Moreover, the 'strong archived elitism' strategy improves the population diversity, which further facilitates the explorative capabilities of the algorithm.
Keywords: optimisation; elitist strategies; machine scheduling problems; MSPs; evolutionary computation; population diversity; strong archived elitism; solution quality.
International Journal of Bio-Inspired Computation, 2020 Vol.15 No.3, pp.135 - 146
Accepted: 15 Dec 2019
Published online: 25 May 2020 *