The effect of elitist fitness-based selection on the escape from local optima

Abstract

This article is published under a Creative Commons CC BY-NC-ND license.Random Search is the baseline that a metaheuristic must improve upon to be worth its added complexity. Random Search, in the form of Hill Climbing, cannot escape from local optima. A key claim of many metaheuristics is that they are able to escape from local optima. However, these claims are poorly tested and often based on imprecise definitions of what it means to escape from a local optimum in continuous domain search spaces. A practical and precise definition for an escape from a local optimum is developed. It is then shown how elitist fitness-based selection can lead to the rejection of exploratory search solutions, and this can cause many popular metaheuristics to degrade into (localized) Random Search in their attempts to escape from local optima. The explosion of new metaheuristics has often been just a repeated re-invention of localized Random Search for the key task of escaping from local optima.Natural Sciences and Engineering Research Council of Canada (NSERC) Discovery Grant – RGPIN-2022-04524