Evolutionary Landscape and Management of Population Diversity

TL;DR

This paper analyzes the importance of maintaining population diversity in evolutionary algorithms, investigates a counter-niching technique to promote constructive diversity, and demonstrates its effectiveness through benchmark tests.

Contribution

It introduces a counter-niching EA method that uses informed genetic operations to enhance diversity and prevent premature convergence.

Findings

Counter-niching approach maintains diversity effectively.

Simulation results show improved search performance.

Method discourages premature local convergence.

Abstract

The search ability of an Evolutionary Algorithm (EA) depends on the variation among the individuals in the population [3, 4, 8]. 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 brief analysis of the EA population diversity issues. Next we present an investigation on a counter-niching EA technique [4] that introduces and maintains constructive diversity in the population. The proposed approach uses informed genetic operations to reach promising, but unexplored or under-explored areas of the search space, while discouraging premature local convergence. Simulation runs on a suite of standard benchmark test functions with Genetic Algorithm (GA) implementation shows promising results.