Proceedings of the 5th International Conference on Genetic Algorithms
A Factorized Distribution Algorithm Using Single Connected Bayesian Networks
PPSN VI Proceedings of the 6th International Conference on Parallel Problem Solving from Nature
The gambler's ruin problem, genetic algorithms, and the sizing of populations
Evolutionary Computation
Fda -a scalable evolutionary algorithm for the optimization of additively decomposed functions
Evolutionary Computation
Learning Bayesian networks with local structure
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
Edge histogram based sampling with local search for solving permutation problems
International Journal of Hybrid Intelligent Systems
A bivariate probabilistic model-building genetic algorithm for graph bipartitioning
Proceedings of the 10th annual conference companion on Genetic and evolutionary computation
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
Parallelization of an evolutionary algorithm on a platform with multi-core processors
EA'09 Proceedings of the 9th international conference on Artificial evolution
A preliminary study on EDAs for permutation problems based on marginal-based models
Proceedings of the 13th annual conference on Genetic and 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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In IDEAs, the probability distribution of a selection of solutions is estimated each generation. From this probability distribution, new solutions are drawn. Through the probability distribution, various relations between problem variables can be exploited to achieve efficient optimization. For permutation optimization, only real valued probability distributions have been applied to a real valued encoding of permutations. In this paper, we present two approaches to estimating marginal product factorized probability distributions in the space of permutations directly. The estimated probability distribution is used to identify crossover positions in a real valued encoding of permutations. The resulting evolutionary algorithm (EA) is capable of more efficient scalable optimization of deceptive permutation problems of a bounded order of difficulty than when real valued probability distributions are used.