Recentering, reanchoring & restarting an evolutionary algorithm

Abstract

Recentering-restarting evolutionary algorithms have been used successfully to evolve epidemic networks. This study develops multiple variations of this algorithm for the purpose of evaluating its use for ordered-gene problems. These variations are called recentering or reanchoring-restarting evolutionary algorithms. Two different adaptive representations were explored that both use generating sets to produce local search operations. The degree of locality is controllable by setting program parameters. The variations and representations are applied to what may be considered the quintessential ordered gene problem, the Travelling Salesman Problem. Two sets of experimental analysis were performed. The first used large problem instances to determine how well this algorithm performs in comparison to benchmarks obtained from the DIMACS TSP implementation challenge. The second used many small problem instances to determine if any one of the recentering/reanchoring-restarting evolutionary algorithms outperforms the others. Variations of the recentering/reanchoring-restarting evolutionary algorithm were comparable to some of the best performing computational intelligence algorithms. In studying the small problem instances, no significant trend was found to suggest that one variation of baseline evolutionary algorithms or recentering/reanchoring-restarting evolutionary algorithms outperformed the others. This study shows that the new algorithms are very useful tools for improving results produced by other heuristics.