Abstract
In spite of the success of evolutionary algorithms for dealing with multi-objective optimization problems (the so-called multi-objective evolutionary algorithms (MOEAs)), their main drawback is the fine-tuning of their parameters, which is normally done in an empirical way (using a trial-and-error process for each problem at hand), and usually has a significant impact on their performance. In this paper, we present a self-adaptation methodology that can be incorporated into any MOEA, in order to allow an automatic fine-tuning of parameters, without any human intervention. In order to validate the proposed mechanism, we incorporate it into the NSGA-II, which is a well-known elitist MOEA and we analyze the performance of the resulting approach. The results reported here indicate that the proposed approach is a viable alternative to self-adapt the parameters of a MOEA.
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References
Eiben, Á.E., Hinterding, R., Michalewicz, Z.: Parameter Control in Evolutionary Algorithms. IEEE Transactions on Evolutionary Computation 3(2), 124–141 (1999)
Lobo, F.G., Lima, C.F., Michalewicz, Z. (eds.): Parameter Setting in Evolutionary Algorithms. Springer, Berlin (2007) ISBN 978-3-540-69431-1
Abbass, H.A.: The Self-Adaptive Pareto Differential Evolution Algorithm. In: Congress on Evolutionary Computation (CEC 2002), vol. 1, pp. 831–836. IEEE Service Center, Piscataway (2002)
Toscano Pulido, G., Coello Coello, C.A.: The Micro Genetic Algorithm 2: Towards Online Adaptation in Evolutionary Multiobjective Optimization. In: Fonseca, C.M., Fleming, P.J., Zitzler, E., Deb, K., Thiele, L. (eds.) EMO 2003. LNCS, vol. 2632, pp. 252–266. Springer, Heidelberg (2003)
Trautmann, H., Ligges, U., Mehnen, J., Preuß, M.: A Convergence Criterion for Multiobjective Evolutionary Algorithms Based on Systematic Statistical Testing. In: Rudolph, G., Jansen, T., Lucas, S., Poloni, C., Beume, N. (eds.) PPSN 2008. LNCS, vol. 5199, pp. 825–836. Springer, Heidelberg (2008)
Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A Fast and Elitist Multiobjective Genetic Algorithm: NSGA–II. IEEE Transactions on Evolutionary Computation 6(2), 182–197 (2002)
Kursawe, F.: A Variant of Evolution Strategies for Vector Optimization. In: Schwefel, H.-P., Männer, R. (eds.) PPSN 1990. LNCS, vol. 496, pp. 193–197. Springer, Heidelberg (1991)
Büche, D., Guidati, G., Stoll, P., Koumoutsakos, P.: Self-Organizing Maps for Pareto Optimization of Airfoils. In: Guervós, J.J.M., Adamidis, P.A., Beyer, H.-G., Fernández-Villacañas, J.-L., Schwefel, H.-P. (eds.) PPSN 2002. LNCS, vol. 2439, pp. 122–131. Springer, Heidelberg (2002)
Tan, K., Lee, T., Khor, E.: Evolutionary Algorithms with Dynamic Population Size and Local Exploration for Multiobjective Optimization. IEEE Transactions on Evolutionary Computation 5(6), 565–588 (2001)
Veldhuizen, D.A.V.: Multiobjective Evolutionary Algorithms: Classifications, Analyses, and New Innovations. PhD thesis, Department of Electrical and Computer Engineering. Graduate School of Engineering. Air Force Institute of Technology, Wright-Patterson AFB, Ohio (1999)
Kumar, R., Rockett, P.: Improved Sampling of the Pareto-Front in Multiobjective Genetic Optimizations by Steady-State Evolution: A Pareto Converging Genetic Algorithm. Evolutionary Computation 10(3), 283–314 (2002)
Abbass, H.A., Sarker, R., Newton, C.: PDE: A Pareto-frontier Differential Evolution Approach for Multi-objective Optimization Problems. In: Proceedings of the Congress on Evolutionary Computation 2001 (CEC 2001), vol. 2, pp. 971–978. IEEE Service Center, Piscataway (2001)
Zhu, Z.Y., Leung, K.S.: Asynchronous Self-Adjustable Island Genetic Algorithm for Multi-Objective Optimization Problems. In: Congress on Evolutionary Computation (CEC 2002), vol. 1, pp. 837–842. IEEE Service Center, Piscataway (2002)
Coello Coello, C.A., Toscano Pulido, G.: A micro-genetic algorithm for multiobjective optimization. In: Zitzler, E., Deb, K., Thiele, L., Coello Coello, C.A., Corne, D.W. (eds.) EMO 2001. LNCS, vol. 1993, pp. 126–140. Springer, Heidelberg (2001)
Martí, L., García, J., Berlanga, A., Molina, J.M.: A Cumulative Evidential Stopping Criterion for Multiobjective Optimization Evolutionary Algorithms. In: Thierens, D. (ed.) 2007 Genetic and Evolutionary Computation Conference (GECCO 2007), vol. 1. ACM Press, London (2007)
Zielinski, K., Laur, R.: Adaptive Parameter Setting for a Multi-Objective Particle Swarm Optimization Algorithm. In: 2007 IEEE Congress on Evolutionary Computation (CEC 2007), pp. 3019–3026. IEEE Press, Singapore (2007)
Myers, R.H., Montgomery, D.C.: Response Surface Methodology-Process and Product Optimization Using Designed Experiments. John Wiley and Sons, Chichester (2002)
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)
Lindman, H.R.: Analysis of variance in complex experimental designs. SIAM Rev. 18(1), 134–137 (1976)
Zitzler, E., Deb, K., Thiele, L.: Comparison of Multiobjective Evolutionary Algorithms: Empirical Results. Evolutionary Computation 8(2), 173–195 (2000)
Deb, K., Thiele, L., Laumanns, M., Zitzler, E.: Scalable Test Problems for Evolutionary Multiobjective Optimization. In: Abraham, A., Jain, L., Goldberg, R. (eds.) Evolutionary Multiobjective Optimization. Theoretical Advances and Applications, pp. 105–145. Springer, USA (2005)
Zitzler, E., Thiele, L., Laumanns, M., Fonseca, C.M., da Fonseca, V.G.: Performance Assessment of Multiobjective Optimizers: An Analysis and Review. IEEE Transactions on Evolutionary Computation 7(2), 117–132 (2003)
Zitzler, E.: Evolutionary Algorithms for Multiobjective Optimization: Methods and Applications. PhD thesis, Swiss Federal Institute of Technology (ETH), Zurich, Switzerland (1999)
Goldberg, D.E.: Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley Publishing Company, Reading (1989)
Deb, K., Agrawal, R.B.: Simulated Binary Crossover for Continuous Search Space. Complex Systems 9, 115–148 (1995)
Michalewicz, Z.: Genetic Algorithms + Data Structures = Evolution Programs, 3rd edn. Springer, Heidelberg (1996)
Syswerda, G.: Uniform Crossover in Genetic Algorithms. In: Schaffer, J.D. (ed.) Proceedings of the Third International Conference on Genetic Algorithms, pp. 2–9. Morgan Kaufmann Publishers, San Mateo (1989)
Schwefel, H.P.: Evolution and Optimum Seeking. John Wiley & Sons, New York (1995)
Zitzler, E., Thiele, L.: Multiobjective Optimization Using Evolutionary Algorithms - A Comparative Case Study. In: Eiben, A.E., Bäck, T., Schoenauer, M., Schwefel, H.-P. (eds.) PPSN 1998. LNCS, vol. 1498, pp. 292–301. Springer, Heidelberg (1998)
Huband, S., Hingston, P., Barone, L., While, L.: A Review of Multiobjective Test Problems and a Scalable Test Problem Toolkit. IEEE Transactions on Evolutionary Computation 10(5), 477–506 (2006)
Li, H., Zhang, Q.: Multiobjective Optimization Problems With Complicated Pareto Sets, MOEA/D and NSGA-II. IEEE Transactions on Evolutionary Computation 13(2), 284–302 (2009)
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Zapotecas Martínez, S., Yáñez Oropeza, E.G., Coello Coello, C.A. (2011). Self-adaptation Techniques Applied to Multi-Objective Evolutionary Algorithms. In: Coello, C.A.C. (eds) Learning and Intelligent Optimization. LION 2011. Lecture Notes in Computer Science, vol 6683. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25566-3_44
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DOI: https://doi.org/10.1007/978-3-642-25566-3_44
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