Opposition-based learning in compact differential evolution

  • Authors:

  • Giovanni Iacca;Ferrante Neri;Ernesto Mininno

  • Affiliations:

  • Department of Mathematical Information Technology, University of Jyväskylä, Agora, Finland;Department of Mathematical Information Technology, University of Jyväskylä, Agora, Finland;Department of Mathematical Information Technology, University of Jyväskylä, Agora, Finland

  • Venue:
  • EvoApplications'11 Proceedings of the 2011 international conference on Applications of evolutionary computation - Volume Part I
  • Year:
  • 2011

Quantified Score

Hi-index 0.00

Visualization

Abstract

This paper proposes the integration of the generalized opposition based learning into compact Differential Evolution frameworks and tests its impact on the algorithmic performance. Opposition-based learning is a technique which has been applied, in several circumstances, to enhance the performance of Differential Evolution. It consists of the generation of additional points by means of a hyper-rectangle. These opposition points are simply generated by making use of a central symmetry within the hyper-rectangle. In the population based Differential Evolution, the inclusion of this search move corrects a limitation of the original algorithm, i.e. the scarcity of search moves, and sometimes leads to benefits in terms of algorithmic performance. The opposition-based learning scheme is further improved in the generalized scheme by integrating some randomness and progressive narrowing of the search. The proposed study shows how the generalized opposition-based learning can be encoded within a compact Differential Evolution framework and displays its effect on a set of diverse problems. Numerical results show that the generalized opposition-based learning is beneficial for compact Differential Evolution employing the binomial crossover while its implementation is not always successful when the exponential crossover is used. In particular, the opposition-based logic appears to be in general promising for non-separable problems whilst it seems detrimental for separable problems.