arXiv:1107.2104v1 [cs.NE] 11 Jul 2011 An estimation of distribution algorithm with adaptive Gibbs sampling for unconstrained global optimization Jon´as Velasco, Mario A. Saucedo-Espinosa, Hugo Jair Escalante, Karlo Mendoza, C´ esar Emilio Villarreal-Rodr´ ıguez, ´ Oscar L. Chac´ on-Mondrag´on,Adri´ an Rodr´ ıguez, Arturo Berrones Posgrado en Ingenier´ ıa de Sistemas, FIME, UANL AP 126, Cd. Universitaria, San Nicol´ as de los Garza, NL 66450, M´ exico jonas.velascoal@uanl.edu.mx July 12, 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 frame- work 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 opti- mization. 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. Key words: Estimation of distribution algorithms; Evolutionary algorithms; Metropolis-within-Gibbs; Global optimization. 1 Introduction The inherent difficulty of global optimization problems lies in finding the very best minimum from a multitude of local minima. We consider the problem 1