Searching under Multi-evolutionary Pressures

  • Hussein A. Abbass
  • Kalyanmoy Deb
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2632)


A number of authors made the claim that a multiobjective approach preserves genetic diversity better than a single objective approach. Sofar, none of these claims presented a thorough analysis to the effect of multiobjective approaches. In this paper, we provide such analysis and show that a multiobjective approach does preserve reproductive diversity. We make our case by comparing a pareto multiobjective approach against a single objective approach for solving single objective global optimization problems in the absence of mutation. We show that the fitness landscape is different in both cases and the multiobjective approach scales faster and produces better solutions than the single objective approach.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    H.A. Abbass. Self-adaptive pareto diffierential evolution. In The IEEE Congress on Evolutionary Computation, Honolulu, USA, pages 831–836. IEEE Press, 2002.Google Scholar
  2. 2.
    H.A. Abbass. A memetic pareto evolutionary approach to artificial neural networks. In M. Stumptner, D. Corbett, and M. Brooks, editors, Proceedings of the 14th Australian Joint Conference on Artificial Intelligence (AI’01), pages 1–12, Berlin, 2001. Springer-Verlag.Google Scholar
  3. 3.
    H.A. Abbass. An evolutionary artificial neural network approach for breast cancer diagnosis. Artificial Intelligence in Medicine, 25(3):265–281, 2002.CrossRefGoogle Scholar
  4. 4.
    S. Bleuler, M. Brack, L. Thiele, and E. Zitzler. Multiobjective genetic programming: Reducing bloat using SPEA2. In Proceedings of the 2001 Congress on Evolutionary Computation CEC 2001, pages 536–543. IEEE Press, 2001.Google Scholar
  5. 5.
    E. De Jong, R. Watson, and J. Pollack. Reducing bloat and promoting diversity using multi-objective methods. In Proceedings of the Genetic and Evolutionary Computation Conference, pages 11–18, 2001.Google Scholar
  6. 6.
    K De Jong. An analysis of the behavior of a class of genetic adaptive systems. PhD thesis, 1975.Google Scholar
  7. 7.
    V.S. Gordon and D. Whitley. Serial and parallel genetic algorithms as function optimization. In The 5th International Conference on Genetic Algortihms, pages 177–183. Morgan Kaufmann, 1993.Google Scholar
  8. 8.
    J. Knowles and D. Corne. Reducing local optima in single-objective problems by multi-objectivization. In First International Conference on Evolutionary Multicriterion Optimization (EMO’01), pages 269–283. Springer-Verlag, 2001.Google Scholar
  9. 9.
    H. Mühlenbein and D. Schlierkamp-Voosen. Predictive models for the breeder genetic algorithms: continuous parameter optimization. Evolutionary Computation, 1(1):25–49, 1993.CrossRefGoogle Scholar
  10. 10.
    H. Mühlenbein and D. Schlierkamp-Voosen. The science of breeding and its application to the breeder genetic algorithm bga. Evolutionary Computation, 1(4):335–360, 1994.CrossRefGoogle Scholar
  11. 11.
    V.K. Vassilev, T.C. Fogarty, and J.F. Miller. Information characteristics and the structure of landscapes. Evolutionary Computation, 8(1):31–61, 2000.CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Hussein A. Abbass
    • 1
  • Kalyanmoy Deb
    • 2
  1. 1.Artifficial Life and Adaptive Robotics (A.L.A.R.) Lab, School of Computer ScienceUniversity of New South Wales, Australian Defence Force Academy CampusCanberraAustralia
  2. 2.Mechanical Engineering DepartmentIndian Institute of Technology, KanpurKanpurIndia

Personalised recommendations