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

Article type: Research Article

Authors: Liu, Shih-Hsi^{a; c; *} | Mernik, Marjan^b | Bryant, Barrett R.^a

Affiliations: [a] Department of Computer and Information Sciences, University of Alabama at Birmingham, USA | [b] Faculty of Electrical Engineering and Computer Science, University of Maribor, Slovenia | [c] Department of Computer Science, California State University, Fresno, USA

Correspondence: [*] Corresponding author. E-mail: [email protected] or [email protected]

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.

Keywords: Evolutionary algorithms, genetic algorithms, entropy, parameter control, exploration, exploitation

DOI: 10.3233/KES-2009-0184

Journal: International Journal of Knowledge-based and Intelligent Engineering Systems, vol. 13, no. 3-4, pp. 185-206, 2009