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Critical Issues in Model-Based Surrogate Functions in Estimation of Distribution Algorithms

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Swarm, Evolutionary, and Memetic Computing (SEMCCO 2013)

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Abstract

In many optimization domains the solution of the problem can be made more efficient by the construction of a surrogate fitness model. Estimation of distribution algorithms (EDAs) are a class of evolutionary algorithms particularly suitable for the conception of model-based surrogate techniques. Since EDAs generate probabilistic models, it is natural to use these models as surrogates. However, there exist many types of models and methods to learn them. The issues involved in the conception of model-based surrogates for EDAs are various and some of them have received scarce attention in the literature. In this position paper, we propose a unified view for model-based surrogates in EDAs and identify a number of critical issues that should be dealt with in order to advance the research in this area.

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Santana, R., Mendiburu, A., Lozano, J.A. (2013). Critical Issues in Model-Based Surrogate Functions in Estimation of Distribution Algorithms. In: Panigrahi, B.K., Suganthan, P.N., Das, S., Dash, S.S. (eds) Swarm, Evolutionary, and Memetic Computing. SEMCCO 2013. Lecture Notes in Computer Science, vol 8298. Springer, Cham. https://doi.org/10.1007/978-3-319-03756-1_1

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  • DOI: https://doi.org/10.1007/978-3-319-03756-1_1

  • Publisher Name: Springer, Cham

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