Abstract
This work studies distribution search in the context of evolutionary many-objective optimization where, in addition to good convergence towards the optimal Pareto front, it is required to find a set of trade-off solutions spread according to a given distribution. We particularly focus on the effectiveness of Adaptive ε-Ranking, which reclassifies sets of non-dominated solutions using iteratively a randomized sampling procedure that applies ε-dominance with a mapping function \(\mathbf{f}(\mathbf{x}){\mapsto }^{\epsilon }\mathbf{{f}}^{\prime}(\mathbf{x})\) to bias selection towards the distribution of solutions implicit in the mapping. We analyze the effectiveness of Adaptive ε-Ranking with three linear mapping functions for ε-dominance and study the importance of recombination to properly guide the algorithm towards the distribution we aim to find. As test problems, we use functions of the DTLZ family with M = 6 objectives, varying the number of variables N from 10 to 50.
An erratum to this chapter can be found at http://dx.doi.org/10.1007/978-3-642-37577-4_18
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Aguirre, H., Oyama, A., Tanaka, K. (2014). Distribution Search on Evolutionary Many-Objective Optimization: Selection Mappings and Recombination Rate. In: Cagnoni, S., Mirolli, M., Villani, M. (eds) Evolution, Complexity and Artificial Life. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37577-4_16
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