Many-objective test problems to visually examine the behavior of multiobjective evolution in a decision space

  • Authors:

  • Hisao Ishibuchi;Yasuhiro Hitotsuyanagi;Noritaka Tsukamoto;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;Department of Computer Science and Intelligent Systems, Graduate School of Engineering, Osaka Prefecture University, Osaka, Japan

  • Venue:
  • PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part II
  • Year:
  • 2010

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Abstract

Many-objective optimization is a hot issue in the EMO (evolutionary multiobjective optimization) community. Since almost all solutions in the current population are non-dominated with each other in many-objective EMO algorithms, we may need a different fitness evaluation scheme from the case of two and three objectives. One difficulty in the design of many-objective EMO algorithms is that we cannot visually observe the behavior of multiobjective evolution in the objective space with four or more objectives. In this paper, we propose the use of many-objective test problems in a two- or three-dimensional decision space to visually examine the behavior of multiobjective evolution. Such a visual examination helps us to understand the characteristic features of EMO algorithms for many-objective optimization. Good understanding of existing EMO algorithms may facilitates their modification and the development of new EMO algorithms for many-objective optimization.