Abstract
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.
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González, C., Ramírez, A., Lozano, J.A., Larrañaga, P. (2005). Average Time Complexity of Estimation of Distribution Algorithms. In: Cabestany, J., Prieto, A., Sandoval, F. (eds) Computational Intelligence and Bioinspired Systems. IWANN 2005. Lecture Notes in Computer Science, vol 3512. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11494669_6
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DOI: https://doi.org/10.1007/11494669_6
Publisher Name: Springer, Berlin, Heidelberg
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