Abstract
In this paper a new “hierarchical” evolutionary approach to solving multi-objective optimization problems is introduced. The results of experiments with standard multi-objective test problems, which were aimed at comparing “hierarchical” and “classical” versions of multi-objective evolutionary algorithms, show that the proposed approach is a very promising technique.
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Keywords
- Evolutionary Algorithm
- Multiobjective Optimization
- Hierarchical Approach
- Pareto Frontier
- Simple Genetic Algorithm
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References
Ciepiela, E., Kocot, J., Siwik, L.: Composable runtime environment for building evolutionary algorithms. Technical report, Department of Computer Science, AGH University of Science and Technology (2006)
Deb, K.: Multi-Objective Optimization using Evolutionary Algorithms. John Wiley & Sons, Chichester (2001)
Fonseca, C., Fleming, P.: Genetic algorithms for multiobjective optimization: Formulation, discussion and generalization. In: Genetic Algorithms: Proceedings of the Fifth International Conference, pp. 416–423. Morgan Kaufmann, San Francisco (1993)
Schaefer, R., Kołodziej, J.: Genetic search reinforced by the population hierarchy. In: Foundations of Genetic Algorithms 7, pp. 383–399. Morgan Kaufman Publisher, San Francisco (2003)
Schaffer, J.D.: Some experiments in machine learning using vector evaluated genetic algorithms. PhD thesis, Vanderbilt University (1984)
Wierzba, B., Semczuk, A., Kołodziej, J., Schaefer, R.: Hierarchical genetic strategy with real number encoding. Technical report, Institute of Computer Science, Jagiellonian University (2003)
Wu, J., Azarm, S.: Metrics for quality assessment of a multiobjective design optimization solution set. Transactions of the ASME, Journal of Mechanical Design 123, 18–25 (2001)
Zitzler, E.: Evolutionary Algorithms for Multiobjective Optimization: Methods and Applications. PhD thesis, Swiss Federal Institute of Technology (2001)
Zitzler, E., Thiele, L.: An evolutionary algorithm for multiobjective optimization: The strength pareto approach. Technical Report 43, Swiss Federal Institute of Technology, Zurich, Gloriastrasse 35, CH-8092 Zurich, Switzerland (1998)
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Ciepiela, E., Kocot, J., Siwik, L., Dreżewski, R. (2008). Hierarchical Approach to Evolutionary Multi-Objective Optimization. In: Bubak, M., van Albada, G.D., Dongarra, J., Sloot, P.M.A. (eds) Computational Science – ICCS 2008. ICCS 2008. Lecture Notes in Computer Science, vol 5103. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-69389-5_82
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DOI: https://doi.org/10.1007/978-3-540-69389-5_82
Publisher Name: Springer, Berlin, Heidelberg
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