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Article overview
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An estimation of distribution algorithm with adaptive Gibbs sampling for unconstrained global optimization | | Jonás Velasco
; Mario A. Saucedo-Espinosa
; Hugo Jair Escalante
; Karlo Mendoza
; César Emilio Villarreal-Rodríguez
; Óscar L. Chacón-Mondragón
; Adrián Rodríguez
; Arturo Berrones
; | | Date: |
11 Jul 2011 | | Abstract: | In this paper is proposed a new heuristic approach belonging to the field of
evolutionary Estimation of Distribution Algorithms (EDAs). EDAs builds a
probability model and a set of solutions is sampled from the model which
characterizes the distribution of such solutions. The main framework of the
proposed method is an estimation of distribution algorithm, in which an
adaptive Gibbs sampling is used to generate new promising solutions and, in
combination with a local search strategy, it improves the individual solutions
produced in each iteration. The Estimation of Distribution Algorithm with
Adaptive Gibbs Sampling we are proposing in this paper is called AGEDA. We
experimentally evaluate and compare this algorithm against two deterministic
procedures and several stochastic methods in three well known test problems for
unconstrained global optimization. It is empirically shown that our heuristic
is robust in problems that involve three central aspects that mainly determine
the difficulty of global optimization problems, namely high-dimensionality,
multi-modality and non-smoothness. | | Source: | arXiv, 1107.2104 | | Services: | Forum | Review | PDF | Favorites | |
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