Advertisement

MOBAIS: A Bayesian Artificial Immune System for Multi-Objective Optimization

  • Pablo A. D. Castro
  • Fernando J. Von Zuben
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5132)

Abstract

Significant progress has been made in theory and design of artificial immune systems (AISs) for solving multi-objective problems accurately. However, an aspect not yet widely addressed by the research reported in the literature is the lack of ability of the AIS to deal effectively with building blocks (high-quality partial solutions coded in the antibody). The available AISs present mechanisms for evolving the population that do not take into account the relationship among the variables of the problem, causing the disruption of these high-quality partial solutions. Recently, we proposed a novel immune-inspired approach for single-objective optimization as an attempt to avoid this drawback. Our proposal replaces the traditional mutation and cloning operators with a probabilistic model, more specifically a Bayesian network representing the joint distribution of promising solutions and, subsequently, uses this model for sampling new solutions. Now, in this paper we extend our methodology for solving multi-objective optimization problems. The proposal, called Multi-Objective Bayesian Artificial Immune System (MOBAIS), was evaluated in the well-known multi-objective Knapsack problem and its performance compares favorably with that produced by contenders such as NSGA-II, MISA, and mBOA.

Keywords

Bayesian Network Pareto Front Multiobjective Optimization Knapsack Problem Multiobjective Optimization Problem 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Ada, G.L., Nossal, G.J.V.: The Clonal Selection Theory. Scientific American 257(2), 50–57 (1987)CrossRefGoogle Scholar
  2. 2.
    Jerne, N.K.: Towards a Network Theory of the Immune System. Ann. Immunol (Inst. Pasteur) 125C, 373–389 (1974)Google Scholar
  3. 3.
    Yoo, J., Hajela, P.: Immune network simulations in multicriterion design. Structural Optimization 18, 85–94 (1999)Google Scholar
  4. 4.
    Coello Coello, C., Cortés, N.C.: An Approach to Solve Multiobjective Optimization Problems Based on an Artificial Immune System. In: First International Conference on Artificial Immune System, pp. 212–221 (2002)Google Scholar
  5. 5.
    Coello Coello, C., Cortés, N.C.: Solving Multiobjective Optimization Problems Using an Artificial Immune System. Genetic Programming and Evolvable Machines 6(2), 163–190 (2005)CrossRefGoogle Scholar
  6. 6.
    Luh, G.-C., Chueh, C.-H., Liu, W.-M.: MOIA: Multi-objective Immune Algorithm. Engineering Optimization 35(2), 143–164 (2003)CrossRefMathSciNetGoogle Scholar
  7. 7.
    Freschi, F., Repetto, M.: VIS: An artificial immune network for multi-objective optimization. Engineering Optimization 38, 975–996 (2006)CrossRefGoogle Scholar
  8. 8.
    Coelho, G.P., Von Zuben, F.J.: Omni-aiNet: An Immune-Inspired Approach for Omni Optimization. In: Bersini, H., Carneiro, J. (eds.) ICARIS 2006. LNCS, vol. 4163, pp. 294–308. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  9. 9.
    Chen, J., Mahfouf, M.: A Population Adaptive Based Immune Algorithm for Solving Multi-objective Optimization Problems. In: Bersini, H., Carneiro, J. (eds.) ICARIS 2006. LNCS, vol. 4163, pp. 280–293. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  10. 10.
    Castro, P.A.D., Von Zuben, F.J.: BAIS: A Bayesian Artificial Immune System for the Effective Handling of Building Blocks. Information Sciences - Special Issue on Artificial Immune System (accepted, 2008)Google Scholar
  11. 11.
    Mühlenbein, H., Paass, G.: From Recombination of Genes to the Estimation of Distributions I. Binary Parameters. In: 4th Int. Conf. on Parallel Problem Solving from Nature, pp. 178–187 (1996)Google Scholar
  12. 12.
    Baluja, S.: Population-Based Incremental Learning: A Method for Integrating Genetic Search Based Function Optimization and Competitive Learning, Technical Report, Carnegie Mellon University, Pittsburgh, PA, USA (1994)Google Scholar
  13. 13.
    Pelikan, M., Goldberg, D., Lobo, F.: A survey of optimization by building and using probabilistic models, Technical Report, University of Illinois, ILLIGAL Report n 99018 (1999)Google Scholar
  14. 14.
    Deb, K., Agrawal, S., Pratap, A., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. Evolutionary Computation 6(2), 182–197 (2002)CrossRefGoogle Scholar
  15. 15.
    Castro, P.A.D., Von Zuben, F.J.: Bayesian Learning of Neural Networks by Means of Artificial Immune Systems. In: 5th Int. Joint Conf. on Neural Networks, pp. 9885–9892 (2006)Google Scholar
  16. 16.
    Cooper, G., Herskovits, E.: A bayesian method for the induction of probabilistic networks from data. Machine Learning 9, 309–347 (1992)zbMATHGoogle Scholar
  17. 17.
    Henrion, M.: Propagating uncertainty in Bayesian networks by probabilistic logic sampling. Uncertainty in Artificial Intelligence 2, 149–163 (1998)Google Scholar
  18. 18.
    Zitzler, E., Thiele, L.: Multiobjective Evolutionary Algorithms: A Comparative Case Study and the Strength Pareto Approach. IEEE Transactions on Evolutionary Computation 3(4), 257–271 (1999)CrossRefGoogle Scholar
  19. 19.
    Van Veldhuizen, D.A.: Multiobjective Evolutionary Algorithms: Classifications, Analysis, and New Innovations, PhD Thesis, Graduate School of Engineering of the Air Force Inst. of Tech., Wright-Patterson AFB (1999)Google Scholar
  20. 20.
    Khan, N., Goldberg, D.E., Pelikan, M.: Multi-Objective Bayesian Optimization Algorithm, Illigal Report 2002009 (2002)Google Scholar
  21. 21.
    Zitzler, E., Deb, K., Thiele, L.: Comparison of Multiobjective Evolutionary Algorithms: Empirical Results. Evolutionary Computation 8(2), 173–195 (2000)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Pablo A. D. Castro
    • 1
  • Fernando J. Von Zuben
    • 1
  1. 1.Laboratory of Bioinformatics and Bioinspired Computing - LBiC Department of Computer Engineering and Industrial Automation - DCA School of Electrical and Computer Engineering - FEECUniversity of Campinas - UNICAMPCampinasBrazil

Personalised recommendations