Diversity Handling In Evolutionary Landscape

TL;DR

This paper analyzes the importance of maintaining diversity in evolutionary algorithms to prevent premature convergence and introduces a counter-niching technique that enhances search effectiveness.

Contribution

It provides a comprehensive analysis of diversity issues in EAs and proposes a novel counter-niching method to sustain constructive diversity during search.

Findings

Counter-niching approach improves exploration of search space.

Simulation results show better performance on benchmark functions.

Informed genetic operations help avoid premature convergence.

Abstract

The search ability of an Evolutionary Algorithm (EA) depends on the variation among the individuals in the population. Maintaining an optimal level of diversity in the EA population is imperative to ensure that progress of the EA search is unhindered by premature convergence to suboptimal solutions. Clearer understanding of the concept of population diversity, in the context of evolutionary search and premature convergence in particular, is the key to designing efficient EAs. To this end, this paper first presents a comprehensive analysis of the EA population diversity issues. Next we present an investigation on a counter-niching EA technique that introduces and maintains constructive diversity in the population. The proposed approach uses informed genetic operations to reach promising, but un-explored or under-explored areas of the search space, while discouraging premature local convergence. Simulation runs on a number of standard benchmark test functions with Genetic Algorithm (GA) implementation shows promising results.