A new orthogonal array based crossover, with analysis of gene interactions, for evolutionary algorithms and its application to car door design

  • Authors:

  • K. Y. Chan;C. K. Kwong;H. Jiang;M. E. Aydin;T. C. Fogarty

  • Affiliations:

  • Digital Ecosystems and Business Intelligence Institute, Curtin University of Technology, Perth, Australia;Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong, PR China;Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong, PR China;Department of Computing and Information Systems, University of Bedfordshire, Luton, United Kingdom;Faculty of Business, Computing and Information Management, London South Bank University, 103 Borough Road, London, United Kingdom

  • Venue:
  • Expert Systems with Applications: An International Journal
  • Year:
  • 2010

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Abstract

Recent research shows that orthogonal array based crossovers outperform standard and existing crossovers in evolutionary algorithms in solving parametrical problems with high dimensions and multi-optima. However, those crossovers employed so far, ignore the consideration of interactions between genes. In this paper, we propose a method to improve the existing orthogonal array based crossovers by integrating information of interactions between genes. It is empirically shown that the proposed orthogonal array based crossover outperforms significantly both the existing orthogonal array based crossovers and standard crossovers on solving parametrical benchmark functions that interactions exist between variables. To further compare the proposed orthogonal array based crossover with the existing crossovers in evolutionary algorithms, a validation test based on car door design is used in which the effectiveness of the proposed orthogonal array based crossover is studied.