Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation
Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation
A Survey of Optimization by Building and Using Probabilistic Models
Computational Optimization and Applications
AllelesLociand the Traveling Salesman Problem
Proceedings of the 1st International Conference on Genetic Algorithms
Genetic Algorithms, Clustering, and the Breaking of Symmetry
PPSN VI Proceedings of the 6th International Conference on Parallel Problem Solving from Nature
Permutation Optimization by Iterated Estimation of Random Keys Marginal Product Factorizations
PPSN VII Proceedings of the 7th International Conference on Parallel Problem Solving from Nature
PPSN VII Proceedings of the 7th International Conference on Parallel Problem Solving from Nature
From Recombination of Genes to the Estimation of Distributions I. Binary Parameters
PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
Population-Based Incremental Learning: A Method for Integrating Genetic Search Based Function Optimization and Competitive Learning
Towards a New Evolutionary Computation: Advances on Estimation of Distribution Algorithms (Studies in Fuzziness and Soft Computing)
Scalable Optimization via Probabilistic Modeling: From Algorithms to Applications (Studies in Computational Intelligence)
Parimutuel Betting on Permutations
WINE '08 Proceedings of the 4th International Workshop on Internet and Network Economics
Triangulation of Bayesian networks with recursive estimation of distribution algorithms
International Journal of Approximate Reasoning
Protein Folding in Simplified Models With Estimation of Distribution Algorithms
IEEE Transactions on Evolutionary Computation
Introducing the mallows model on estimation of distribution algorithms
ICONIP'11 Proceedings of the 18th international conference on Neural Information Processing - Volume Part II
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Estimation of Distribution Algorithms are a class of evolutionary algorithms characterized by the use of probabilistic models. These algorithms have been applied successfully to a wide set of artificial and real-world problems, achieving competitive results in most scenarios. Nevertheless, there are some problems whose solutions can be naturally represented as a permutation, for which EDAs have not been extensively developed. Although some work has been done in this area, most of the approaches are adaptations of EDAs designed for problems based on integer or real domains, and only a few algorithms have been specifically designed to deal with permutation-based problems. In this paper, we present an EDA that learns probability distributions over permutations. Particularly, our approach is based on the use of k-order marginals. In addition, we carry out some preliminary experiments over classical permutation-based problems in order to study the performance of the proposed k-order marginals EDA.