Incorporation of scalarizing fitness functions into evolutionary multiobjective optimization algorithms

  • Authors:

  • Hisao Ishibuchi;Tsutomu Doi;Yusuke Nojima

  • Affiliations:

  • Department of Computer Science and Intelligent Systems, Graduate School of Engineering, Osaka Prefecture University, Osaka, Japan;Department of Computer Science and Intelligent Systems, Graduate School of Engineering, Osaka Prefecture University, Osaka, Japan;Department of Computer Science and Intelligent Systems, Graduate School of Engineering, Osaka Prefecture University, Osaka, Japan

  • Venue:
  • PPSN'06 Proceedings of the 9th international conference on Parallel Problem Solving from Nature
  • Year:
  • 2006

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Abstract

This paper proposes an idea of probabilistically using a scalarizing fitness function in evolutionary multiobjective optimization (EMO) algorithms. We introduce two probabilities to specify how often the scalarizing fitness function is used for parent selection and generation update in EMO algorithms. Through computational experiments on multiobjective 0/1 knapsack problems with two, three and four objectives, we show that the probabilistic use of the scalarizing fitness function improves the performance of EMO algorithms. In a special case, our idea can be viewed as the probabilistic use of an EMO scheme in single-objective evolutionary algorithms (SOEAs). From this point of view, we examine the effectiveness of our idea. Experimental results show that our idea improves not only the performance of EMO algorithms for multiobjective problems but also that of SOEAs for single-objective problems.