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Designing Multi-objective Variation Operators Using a Predator-Prey Approach

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Evolutionary Multi-Criterion Optimization (EMO 2007)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 4403))

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Abstract

In this paper, we propose a new conceptual method for the design, investigation, and evaluation of multi-objective variation operators for evolutionary multi-objective algorithms. To this end, we apply a modified predator-prey model that allows an independent analysis of different operators. Using this model problem specific operators can be combined to more complex operators. Additionally, we review the simplex recombination, a new rotation-independent recombination scheme, and examine its impact concerning our design method. We show exemplarily as a first attempt the advantageous combination of several standard variation operators that lead to better results for selected test functions.

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References

  1. Bäck, T., Schwefel, H.-P.: An Overview of Evolutionary Algorithms for Parameter Optimization. Evolutionary Computation 1(1), 1–23 (1993)

    Article  Google Scholar 

  2. Bartz-Beielstein, T., Lasarczyk, C., Preuss, M.: Sequential parameter optimization. In: Proceedings of 2005 Congress on Evolutionary Computation, vol. 1, pp. 773–780. IEEE Press, Piscataway (2005)

    Chapter  Google Scholar 

  3. Büche, D., Müller, S., Koumoutsakos, P.: Self-Adaptation for Multi-objective Evolutionray Algorithms. In: Fonseca, C.M., Fleming, P.J., Zitzler, E., Deb, K., Thiele, L. (eds.) EMO 2003. LNCS, vol. 2632, pp. 267–281. Springer, Heidelberg (2003)

    Chapter  Google Scholar 

  4. Deb, K.: Multi-Objective Optimization using Evolutionary Algorithms. Wiley-Interscience Series in Systems and Optimization. Wiley, Chichester (2001)

    MATH  Google Scholar 

  5. Eshelman, L.J., Schaffer, J.D.: Real-coded genetic algorithms and interval-schemata. In: Second Workshop on Foundations of Genetic Algorithms, pp. 187–202. Morgan Kaufmann, San Francisco (1992)

    Google Scholar 

  6. Grimme, C., Schmitt, K.: Inside a Predator-Prey Model for Multi-Objective Optimization: A Second Study. In: Beyer, H.-G., et al. (eds.) Genetic and Evolutionary Computation Conference, pp. 707–714. ACM Press, New York (2006)

    Google Scholar 

  7. Hanne, T.: On the convergence of multiobjective evolutionary algorithms. European Journal of Operational Research 117, 553–564 (1999)

    Article  MATH  Google Scholar 

  8. Kursawe, F.: A Variant of Evolution Strategies for Vector Optimization. In: Schwefel, H.-P., Männer, R. (eds.) Parallel Problem Solving from Nature. LNCS, vol. 496, pp. 193–197. Springer, Heidelberg (1991)

    Chapter  Google Scholar 

  9. Laumanns, M., Rudolph, G., Schwefel, H.-P.: A Spatial Predator-Prey Approach to Multi-Objective Optimization: A Preliminary Study. In: Bäck, T., Eiben, A.E., Schoenauer, M., Schwefel, H.-P. (eds.) Parallel Problem Solving From Nature V, pp. 241–249. Springer, Berlin (1998)

    Chapter  Google Scholar 

  10. Li, X.: A Real-Coded Predator-Prey Genetic Algorithm for Multiobjective Optimization. In: Evolutionary Multi-Criterion Optimization Conference, pp. 207–221 (2003)

    Google Scholar 

  11. Naujoks, B., Beume, N., Emmerich, M.: Metamodel-assisted SMS-EMOA applied to airfoil optimization tasks. In: Schilling, R., Haase, W., Périaux, J., Baier, H. (eds.) EUROGEN 2005 (CD-ROM), FLM, TU München (2005)

    Google Scholar 

  12. Rudenko, O., Schoenauer, M.: Dominance based crossover operator for evolutionary multi-objective algorithms. In: Yao, X., Burke, E.K., Lozano, J.A., Smith, J., Merelo-Guervós, J.J., Bullinaria, J.A., Rowe, J.E., Tiňo, P., Kabán, A., Schwefel, H.-P. (eds.) Parallel Problem Solving from Nature - PPSN VIII. LNCS, vol. 3242, pp. 812–821. Springer, Heidelberg (2004)

    Google Scholar 

  13. Rudolph, G.: On a Multi-Objective Evolutionary Algorithm and Its Convergence to the Pareto Set. In: Proc. of the 5th IEEE CEC, pp. 511–516. IEEE Press, Piscataway (1998)

    Google Scholar 

  14. Schmitt, K., Mehnen, J., Michelitsch, T.: Using predators and preys in evolution strategies. In: Genetic and Evolutionary Computation Conference, vol. 1, pp. 827–828. ACM Press, New York (2005)

    Google Scholar 

  15. Schwefel, H.-P.: Evolution and Optimum Seeking. Wiley, New York (1995)

    Google Scholar 

  16. Schwefel, H.-P., Rudolph, G.: Contemporary evolution strategies. In: Moran, F., Merelo, J.J., Moreno, A., Chacon, P. (eds.) Advances in Artificial Life. LNCS, vol. 929, pp. 893–907. Springer, Heidelberg (1995)

    Google Scholar 

  17. Tomassini, M.: Spatially Structured Evolutionary Algorithms: Artificial Evolution in Space and Time. Natural Computing Series. Springer, Heidelberg (2005)

    MATH  Google Scholar 

  18. Zitzler, E., Laumanns, M., Thiele, L.: SPEA2: Improving the Strength Pareto Evolutionary Algorithm. Technical Report 103, Computer Engineering and Communication Networks Lab (TIK), Swiss Federal Institute of Technology (ETH), Zürich (2001)

    Google Scholar 

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Shigeru Obayashi Kalyanmoy Deb Carlo Poloni Tomoyuki Hiroyasu Tadahiko Murata

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Grimme, C., Lepping, J. (2007). Designing Multi-objective Variation Operators Using a Predator-Prey Approach. In: Obayashi, S., Deb, K., Poloni, C., Hiroyasu, T., Murata, T. (eds) Evolutionary Multi-Criterion Optimization. EMO 2007. Lecture Notes in Computer Science, vol 4403. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-70928-2_6

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  • DOI: https://doi.org/10.1007/978-3-540-70928-2_6

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-70927-5

  • Online ISBN: 978-3-540-70928-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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