Drift analysis and average time complexity of evolutionary algorithms
Artificial Intelligence
Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation
Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation
PPSN III Proceedings of the International Conference on Evolutionary Computation. The Third Conference on Parallel Problem Solving from Nature: 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
Combinatorial optimization by learning and simulation of Bayesian networks
UAI'00 Proceedings of the Sixteenth conference on Uncertainty in artificial intelligence
Analysis of computational time of simple estimation of distribution algorithms
IEEE Transactions on Evolutionary Computation
Δ-Entropy: Definition, properties and applications in system identification with quantized data
Information Sciences: an International Journal
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This paper presents a study based on the empirical results of the average first hitting time of Estimation of Distribution Algorithms. The algorithms are applied to one example of linear, pseudo-modular, and unimax functions. By means of this study, the paper also addresses recent issues in Estimation of Distribution Algorithms: (i) the relationship between the complexity of the probabilistic model used by the algorithm and its efficiency, and (ii) the matching between this model and the relationship among the variables of the objective function. After analyzing the results, we conclude that the order of convergence is not related to the complexity of the probabilistic model, and that an algorithm whose probabilistic model mimics the structure of the objective function does not guarantee a low order of convergence.