Abstract
Improved sample-based trade-off surface representations for large numbers of performance criteria can be achieved by dividing the global problem into groups of independent, parallel sub-problems, where possible. This paper describes a progressive criterion-space decomposition methodology for evolutionary optimisers, which uses concepts from parallel evolutionary algorithms and nonparametric statistics. The method is evaluated both quantitatively and qualitatively using a rigorous experimental framework. Proof-of-principle results confirm the potential of the adaptive divide-and-conquer strategy.
Keywords
- Decision Variable
- Candidate Solution
- Baseline Algorithm
- Multiobjective Evolutionary Algorithm
- Ideal Vector
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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References
Purshouse, R.C., Fleming, P.J.: Conflict, Harmony, and Independence: Relationships in Evolutionary Multi-Criterion Optimisation. This volume. (2003)
Purshouse, R.C., Fleming, P.J.: Why use Elitism and Sharing in a Multi-Objective Genetic Algorithm? Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2002). (2002) 520–527
Zitzler, E., Laumanns, M., Thiele, L.: SPEA2: Improving the Strength Pareto Evolutionary Algorithm. TIK-Report 103, ETH Zürich. (2001)
Fonseca, C.M., Fleming, P.J.: Genetic algorithms for multiobjective optimization: Formulation, discussion and generalization. Proceedings of the Fifth International Conference on Genetic Algorithms. (1993) 416–423
Deb, K., Agrawal, R.B.: Simulated binary crossover for continuous search space. Compex Systems 9 (1995) 115–148
Deb, K., Goyal, M.: A combined genetic adaptive search (GeneAS) for engineering design. Computer Science and Informatics 26 (1996) 30–45
Zitzler, E., Deb, K., Thiele, L.: Comparison of Multiobjective Evolutionary Algorithms: Empirical Results. Evolutionary Computation 8 (2000) 173–195
Deb, K., Thiele, L., Laumanns, M., Zitzler, E.: Scalable Multi-Objective Optimization Test Problems. Proceedings of the 2002 IEEE Congress on Evolutionary Computation (CEC 2002), Vol. 1. (2002) 825–830
Zitzler, E.: Evolutionary Algorithms for Multiobjective Optimization: Methods and Applications. Doctoral dissertation, ETH Zürich. (1999)
Zitzler, E., Laumanns, M., Thiele, L., Fonseca, C.M., Grunert da Fonseca, V.: Why Quality Assessment Of Multiobjective Optimizers Is Difficult. Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2002). (2002) 666–674
Knowles, J.D., Corne, D. W.: On Metrics for Comparing Nondominated Sets. Proceedings of the 2002 IEEE Congress on Evolutionary Computation (CEC 2002), Vol. 1. (2002) 711–716
Fonseca, C.M., Fleming, P.J.: On the Performance Assessment and Comparison of Stochastic Multiobjective Optimizers. Parallel Problem Solving from Nature — PPSN IV. Lecture Notes in Computer Science, Vol. 1141 (1996) 584–593
Knowles, J.D., Corne, D.W.: Approximating the non-dominated front using the Pareto archived evolution strategy. Evolutionary Computation 8 (2000) 149–172
Manly, B.F.J.: Randomization and Monte Carlo Methods in Biology. Chapman and Hall, London New York Tokyo Melbourne Madras (1991)
Hollander, M., Wolfe, D.A.: Nonparametric Statistical Methods. 2nd edn. Wiley, New York Chichester Weinheim Brisbane Singapore Toronto (1999)
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Purshouse, R.C., Fleming, P.J. (2003). An Adaptive Divide-and-Conquer Methodology for Evolutionary Multi-criterion Optimisation. In: Fonseca, C.M., Fleming, P.J., Zitzler, E., Thiele, L., Deb, K. (eds) Evolutionary Multi-Criterion Optimization. EMO 2003. Lecture Notes in Computer Science, vol 2632. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-36970-8_10
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DOI: https://doi.org/10.1007/3-540-36970-8_10
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