Abstract
This paper presents a method for approximating the Pareto front of a given function using Artificial Immune Networks. The proposed algorithm uses cloning and mutation to create local subsets of the Pareto front, and combines elements of these local fronts in a way that maximizes the diversity. The method is compared against SPEA and NSGA-II in a number of problems from the ZDT test suite, yielding satisfactory results.
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References
Michalewicz, Z.: Genetic Algorithms + Data Structures = Evolution Programs. Springer, New York (1992)
Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multi-objective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 6(2), 181–197 (2002)
Zitzler, E., Thiele, L.: Multiobjective evolutionary algorithms: A comparative case study and the strength pareto approach. IEEE Trans. Evol. Comput. 3(4), 257–271 (1999)
Zitzler, E., Laumanns, M., Thiele, L.: SPEA2: Improving the strength pareto evolutionary algorithm. Tech. Rep. 103, Gloriastrasse 35, CH-8092 Zurich, Switzerland (2001)
Zitzler, E., Deb, K., Thiele, L.: Comparison of multiobjective evolutionary algorithms: Empirical results. IEEE Trans. Evol. Comput. 8(2), 173–195 (2000)
Zitzler, E.: Evolutionary algorithms for multiobjective optimization: Methods and applications. Ph.D. dissertation, Swiss Federal Institute of Technology (1999)
Garrett, S.M.: How Do We Evaluate Artificial Immune Systems? MIT Press Evolutionary Computation 13(2), 145–178 (2005)
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Lanaridis, A., Stafylopatis, A. (2010). An Artificial Immune Network for Multi-objective Optimization. In: Diamantaras, K., Duch, W., Iliadis, L.S. (eds) Artificial Neural Networks – ICANN 2010. ICANN 2010. Lecture Notes in Computer Science, vol 6353. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15822-3_65
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DOI: https://doi.org/10.1007/978-3-642-15822-3_65
Publisher Name: Springer, Berlin, Heidelberg
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