Summary
This paper presents a real-coded estimation distribution algorithm (EDA) inspired to the extended compact genetic algorithm (ECGA) and the real-coded Bayesian Optimization Algorithm (rBOA). Like ECGA, the proposed algorithm partitions the problem variables into a set of clusters that are manipulated as independent variables and estimates the population distribution using marginal product models (MPMs); like rBOA, it employs finite mixtures of models and it does not use any sort of discretization. Accordingly, the proposed real-coded EDA can be either viewed as the extension of the ECGA to real-valued domains by means of finite mixture models or as a simplification of the real-coded BOA to the marginal product models (MPMs). The results reported here show that the number of evaluations required by the proposed algorithm scales sub-quadratically with the problem size in additively separable problems.
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Lanzi, P.L., Nichetti, L., Sastry, K., Voltini, D., Goldberg, D.E. (2008). Real-Coded Extended Compact Genetic Algorithm Based on Mixtures of Models. In: Chen, Yp., Lim, MH. (eds) Linkage in Evolutionary Computation. Studies in Computational Intelligence, vol 157. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-85068-7_14
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DOI: https://doi.org/10.1007/978-3-540-85068-7_14
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