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• W4285588105 abstract "Abstract Estimation of Distribution Algorithms have been successfully used to solve permutation-based Combinatorial Optimization Problems. In this case, the algorithms use probabilistic models specifically designed for codifying probability distributions over permutation spaces. One class of these probability models are distance-based exponential models, and one example of this class is the Mallows model. In spite of its practical success, the theoretical analysis of Estimation of Distribution Algorithms for permutation-based Combinatorial Optimization Problems has not been developed as extensively as it has been for binary problems. With this motivation, this paper presents a first mathematical analysis of the convergence behavior of Estimation of Distribution Algorithms based on Mallows models. The model removes the randomness of the algorithm in order to associate a dynamical system to it. Several scenarios of increasing complexity with different fitness functions and initial probability distributions are analyzed. The obtained results show: a) the strong dependence of the final results on the initial population, and b) the possibility to converge to non-degenerate distributions even in very simple scenarios, which has not been reported before in the literature." @default.
• W4285588105 created "2022-07-16" @default.
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• W4285588105 date "2022-07-16" @default.
• W4285588105 modified "2023-10-05" @default.
• W4285588105 title "A mathematical analysis of EDAs with distance-based exponential models" @default.
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• W4285588105 doi "https://doi.org/10.1007/s12293-022-00371-y" @default.
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