Specification of Genetic Search Directions in Cellular Multi-objective Genetic Algorithms

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1993)


When we try to implement a multi-objective genetic algorithm (MOGA) with variable weights for finding a set of Pareto optimal solutions, one difficulty lies in determining appropriate search directions for genetic search. In our MOGA, a weight value for each objective in a scalar fitness function was randomly specified. Based on the fitness function with the randomly specified weight values, a pair of parent solutions are selected for generating a new solution by genetic operations. In order to find a variety of Pareto optimal solutions of a multi-objective optimization problem, weight vectors should be distributed uniformly on the Pareto optimal surface. In this paper, we propose a proportional weight specification method for our MOGA and its variants. We apply the proposed weight specification method to our MOGA and a cellular MOGA for examining its effect on their search ability.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Goldberg, D.E.: Genetic Algorithms in Search, Optimization, and Machine Learning. Reading, MA: Addison-Wesley (1989).Google Scholar
  2. 2.
    Schaffer, J.D.: Multi-objective optimization with vector evaluated genetic algorithms. Proc. of 1st Int’l Conf. on Genetic Algorithms (1985) 93–100.Google Scholar
  3. 3.
    Kursawe, F.: A variant of evolution strategies for vector optimization. In H.-P. Schwefel and R. Männer (Eds.), Parallel Problem Solving from Nature, Springer-Verlag, Berlin (1991) 193–197.CrossRefGoogle Scholar
  4. 4.
    Horn, J., Nafpliotis, N. and Goldberg, D.E.: A niched Pareto genetic algorithm for multiobjective optimization. Proc. of 1st IEEE Int’l Conf. on Evolutionary Computation (1994) 82–87.Google Scholar
  5. 5.
    Fonseca, C. M. and Fleming, P. J.: An overview of evolutionary algorithms in multiobjective optimization, Evolutionary Computation 3 (1995) 1–16.CrossRefGoogle Scholar
  6. 6.
    Murata, T. and Ishibuchi, H.: Multi-objective genetic algorithm and its applications to flowshop scheduling. International Journal of Computers and Engineering 30, 4 (1996) 957–968.Google Scholar
  7. 7.
    Zitzler, E. and Thiele, L.: Multiobjective evolutionary algorithms: A comparative case study and the strength Pareto Approach. IEEE Trans. on Evolutionary Computation 3 (1999) 257–271.CrossRefGoogle Scholar
  8. 8.
    Ishibuchi, H. and Murata, T.: A multi-objective genetic local search algorithms and its application to flowshop scheduling. IEEE Trans. on System, Man, and Cybernetics, Part C 28 (1998) 392–403.CrossRefGoogle Scholar
  9. 9.
    Murata, T. and Gen, M.: Cellular genetic algorithm for multi-objective optimization. Proc. of 4th Asian Fuzzy System Symposium (2000) 538–542.Google Scholar
  10. 10.
    Murata, T., Ishibuchi, H., and Gen, M.: Cellular genetic local search for multi-objective optimization. Proc. of the Genetic and Evolutionary Computation Conference 2000 (2000) 307–314.Google Scholar
  11. 11.
    Whitley, D.: Cellular Genetic Algorithms. Proc. of 5th Int’l Conf. on Genetic Algorithms (1993) 658.Google Scholar
  12. 12.
    Manderick, B. and Spiessens, P.: Fine-grained parallel genetic algorithms. Proc. of 3rd Int’l Conf. on Genetic Algorithms (1989) 428–433.Google Scholar
  13. 13.
    Wilson, D. S.: Structured demes and the evolution of group-advantageous traits. The American Naturalist 111 (1977) 157–185.CrossRefGoogle Scholar
  14. 14.
    Dugatkin, L. A. and Mesterton-Gibbons, M.: Cooperation among unrelated individuals: Reciprocal altruism, by-product mutualism and group selection in fishes. BioSystems 37 (1996) 19–30.CrossRefGoogle Scholar
  15. 15.
    Nowak, M. A. and May, M.: Evolutionary games and spatial chaos. Nature 359 (1992) 826–859.CrossRefGoogle Scholar
  16. 16.
    Wilson, D. S., Pollock, G. B., and Dugatkin, L. A.: Can altruism evolve in purely viscous populations? Evolutionary Ecology 6 (1992) 331–341.CrossRefGoogle Scholar
  17. 17.
    Oliphant, M.: Evolving cooperation in the non-iterated Prisoner’s Dilemma: The importance of spatial organization. in R. A. Brooks and P. Maes (Eds.), Artificial Life IV, MIT Press, Cambridge (1994) 349–352.Google Scholar
  18. 18.
    Grim, P.: Spatialization and greater generosity in the stochastic Prisoner’s Dilemma. BioSystems 37 (1996) 3–17.CrossRefGoogle Scholar
  19. 19.
    Ishibuchi, H., Nakari, T., and Nakashima T.: Evolution of Strategies in Spatial IPD Games with Structured Demes, Proc. of the Genetic and Evolutionary Computation Conference 2000 (2000).Google Scholar
  20. 20.
    Knowles, J.D., and Corne, D.W.: Approximating the nondominated front using the Pareto Archived Evolution Strategy, Evolutionary Computation (MIT Press), 8, 2 (2000) 149–172.CrossRefGoogle Scholar
  21. 21.
    Johnson, S.M.: Optimal two-and three-stage production schedules with setup times included. Naval Research Logistics Quarterly 1 (1954) 61–68.CrossRefGoogle Scholar
  22. 22.
    Daniels, R.L. and Chambers, R.J.: Multiobjective flow-shop scheduling. Naval Research Logistics 37 (1990) 981–995.zbMATHCrossRefGoogle Scholar
  23. 23.
    Esbensen, H.: Defining solution set quality. Memorandum (No.UCB/ERL M96/1, Electric Research Laboratory, College of Engineering, Univ. of California, Berkeley, USA, Jan., 1996).Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2001

Authors and Affiliations

  1. 1.Department of Industrial and Information Systems EngineeringAshikaga Institute of TechnologyAshikagaJapan
  2. 2.Department of Industrial EngineeringOsaka Prefecture UniversityOsakaJapan

Personalised recommendations