Abstract
This work focuses on the working principles, behavior, and performance of state of the art multiobjective evolutionary algorithms (MOEAs) on discrete search spaces by using MNK-Landscapes. Its motivation comes from the performance shown by NSGA-II and SPEA2 on epistatic problems, which suggest that simpler population-based multiobjective random one-bit climbers are by far superior. Adaptive evolution is a search process driven by selection, drift, mutation, and recombination over fitness landscapes. We group MOEAs features and organize our study around these four important and intertwined processes in order to understand better their effects and clarify the reasons to the poor performance shown by NSGA-II and SPEA2. This work also constitutes a valuable guide for the practitioner on how to set up its algorithm and gives useful insights on how to design more robust and efficient MOEAs.
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References
Kauffman, S.A.: The Origins of Order: Self-Organization and Selection in Evolution. Oxford University Press, Oxford (1993)
Heckendorn, R., Rana, S., Whitley, D.: Test Function Generators as Embedded Landscapes. In: Foundations of Genetic Algorithms, vol. 5, pp. 183–198. Morgan Kaufmann, San Francisco (1999)
Smith, R., Smith, J.: An Examination of Tunable, Random Search Landscapes. In: Foundations of Genetic Algorithms, vol. 5, pp. 165–182. Morgan Kaufmann, San Francisco (1999)
Mathias, K.E., Eshelman, L.J., Schaffer, D.: Niches in NK-landscapes. In: Foundations of Genetic Algorithms, vol. 6, Morgan Kaufmann, San Francisco (2001)
Aguirre, H., Tanaka, K.: A Study on the Behavior of Genetic Algorithms on NK-Landscapes: Effects of Selection, Drift, Mutation, and Recombination. IEICE Trans. Fundamentals 9, 2270–2279 (2003)
Aguirre, H., Tanaka, K.: Insights on Properties of Multiobjective MNK-Landscapes. In: Proc. 2004 IEEE Congress on Evolutionary Computation, pp. 196–203. IEEE Press, Los Alamitos (2004)
Deb, K., Thiele, L., Laumanns, M., Zitzler, E.: Scalable Multi-Objective Optimization Test Problems. In: Proc. 2002 Congress on Evolutionary Computation, pp. 825–830. IEEE Press, Los Alamitos (2002)
Aguirre, H., Tanaka, K.: Effects of Elitism and Population Climbing on Multiobjective MNK-Landscapes. In: Proc. 2004 IEEE Congress on Evolutionary Computation, pp. 449–456. IEEE Press, Los Alamitos (2004)
Aguirre, H., Sato, M., Tanaka, K.: Preliminary Study on the Performance of Multiobjective Evolutionary Algorithms with MNK-Landscapes. In: Proc. RISP Intl. Workshop on Nonlinear Circuits and Signal Processing, pp. 315–318 (2004)
Deb, K.: Multi-Objective Optimization using Evolutionary Algorithms. John Wiley & Sons, Chichester (2001)
Zitzler, E., Laumanns, M., Thiele, L.: SPEA2: Improving the Strength Pareto Evolutionary Algorithm, Technical Report 103, TIK-Report (2001)
Coello, C., Van Veldhuizen, D., Lamont, G.: Evolutionary Algorithms for Solving Multi-Objective Problems. Kluwer Academic Publishers, Boston (2002)
Zitzler, E.: Evolutionary Algorithms for Multiobjective Optimization: Methods and Applications, PhD thesis, Swiss Federal Institute of Technology, Zurich (1999)
Knowles, J., Corne, D.: On Metrics for Comparing Non-dominated Sets. In: Proc. 2002 Congress on Evolutionary Computation, pp. 711–716. IEEE Press, Los Alamitos (2002)
Fleischer, M.: The Measure of Pareto Optima: Applications to Multi-objective Metaheuristics. In: Fonseca, C.M., Fleming, P.J., Zitzler, E., Deb, K., Thiele, L. (eds.) EMO 2003. LNCS, vol. 2632, pp. 519–533. Springer, Heidelberg (2003)
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Aguirre, H.E., Tanaka, K. (2005). Selection, Drift, Recombination, and Mutation in Multiobjective Evolutionary Algorithms on Scalable MNK-Landscapes. In: Coello Coello, C.A., HernĂ¡ndez Aguirre, A., Zitzler, E. (eds) Evolutionary Multi-Criterion Optimization. EMO 2005. Lecture Notes in Computer Science, vol 3410. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-31880-4_25
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DOI: https://doi.org/10.1007/978-3-540-31880-4_25
Publisher Name: Springer, Berlin, Heidelberg
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