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

  • Authors:

  • Shih-Hsi Liu;Marjan Mernik;Barrett R. Bryant

  • Affiliations:

  • (Correspd. E-mail: liush@cis.uab.edu or shliu@csufresno.edu) Department of Computer and Information Sciences, University of Alabama at Birmingham, USA and Department of Computer Science, Californi ...;Faculty of Electrical Engineering and Computer Science, University of Maribor, Slovenia;Department of Computer and Information Sciences, University of Alabama at Birmingham, USA

  • Venue:
  • International Journal of Knowledge-based and Intelligent Engineering Systems
  • Year:
  • 2009

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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.