Abstract
This paper presents a copula-based estimation of a distribution algorithm with parameter updating for numeric optimization problems. This model implements an estimation of a distribution algorithm using a multivariate extension of Clayton’s bivariate copula (MEC-EDA) to estimate the conditional probability for generating a population of individuals. Moreover, the model uses traditional mutation and elitism operators jointly with a heuristic for a population restarting in the evolutionary process. We show that these approaches improve the overall performance of the optimization compared to other copula-based EDAs.
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de Mello, H.D., da Cruz, A.V.A., Vellasco, M.M.B.R. (2014). Nonconvex Functions Optimization Using an Estimation of Distribution Algorithm Based on a Multivariate Extension of the Clayton Copula. In: Corchado, E., Lozano, J.A., Quintián, H., Yin, H. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2014. IDEAL 2014. Lecture Notes in Computer Science, vol 8669. Springer, Cham. https://doi.org/10.1007/978-3-319-10840-7_39
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DOI: https://doi.org/10.1007/978-3-319-10840-7_39
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-10839-1
Online ISBN: 978-3-319-10840-7
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