A self-adaptive mutations with multi-parent crossover evolutionary algorithm for solving function optimization problems

  • Authors:

  • Guangming Lin;Lishan Kang;Yuping Chen;Bob McKay;Ruhul Sarker

  • Affiliations:

  • Shenzhen Inititute of Information Technology, Shenzhen, China and School of Information Technology and Electrical Engineering, UNSW, Australian Defence Force Academy, Canberra, ACT, Australia;School of Computer Science, China University of Geosciences Wuhan, Wuhan, China and State Key Laboratory of Software Engineering, Wuhan University, Wuhan, China;State Key Laboratory of Software Engineering, Wuhan University, Wuhan, China;School of Computer Science & Engineering, College of Engineering, Seoul Natioinal University;School of Information Technology and Electrical Engineering, UNSW, Australian Defence Force Academy, Canberra, ACT, Australia

  • Venue:
  • ISICA'07 Proceedings of the 2nd international conference on Advances in computation and intelligence
  • Year:
  • 2007

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Abstract

In this paper, we introduce a new self-adaptive evolutionary algorithm for solving function optimization problems. The capabilities of the new algorithm include: a) self-adaptive choice of Gaussian or Cauchy mutation to balance the local and global search on the variable subspace, b) using multi-parent crossover to exchange global search information, c) using the best individual to take place the worst individual selection strategy to reduce the selection pressure and ensure to find a global optimization. These enhancements increase the capabilities of the algorithm to solve Shekel problems in a more robust and universal way. This paper will present some results of numerical experiments which show that the new algorithm is more robust and universal than its competitors.