To explore or to exploit: An entropy-driven approach for evolutionary algorithms

Abstract

An evolutionary algorithm is an optimization process comprising two important aspects: exploration discovers potential offspring in new search regions; and exploitation utilizes promising solutions already identified. Intelligent balance between these two aspects may drive the search process towards better fitness results and/or faster convergence rates. Yet, how and when to control the balance perceptively have not yet been comprehensively addressed. This paper introduces an entropy-driven approach for evolutionary algorithms. Five kinds of entropy to express diversity are presented; and the balance between exploration and exploitation is adaptively controlled by one kind of entropy and mutation rate in a metaprogramming fashion. The experimental results of the benchmark functions show that the entropy-driven approach achieves explicit balance between exploration and exploitation and hence obtains even better fitness values and/or convergence rates.