Journal of Intelligent & Fuzzy Systems 36 (2019) 4957–4967 DOI:10.3233/JIFS-179042 IOS Press 4957 Cellular Estimation Gaussian Algorithm for Continuous Domain Yoan Mart´ ınez-L´ opez a , Julio Madera a , Ansel Y. Rodr´ ıguez-Gonz´ alez b,c,∗ and Stephen Barigye d a Department of Computer Sciences, Faculty of Informatics, Camag¨ uey University, Camag¨ uey City, Camag¨ uey, Cuba b Mexican National Research Council (CONACyT), Mexico c CICESE-UT3, Ciudad del Conocimiento, Tepic, Nayarit, M´ exico d Department of Chemistry, McGill University, Montreal, Canada Abstract. Optimization algorithms are important in problems of pattern recognition and artificial intelligence, i.e., the image recognition, face recognition, data analysis, optical recognition, etc. Estimation distribution algorithms (EDAs) is kind of optimization algorithms based on substituting the crossover and mutation operators of the Genetic Algorithms by the estimation and later sampling the probability distribution learned from the selected individuals. However, a weakness of these algorithms is the efficiency in terms of the number of evaluations of the fitness function. In this paper, a Cellular Gaussian Estimation Algorithm (CEGA) for solving continuous optimization problems is proposed. CEGA is derived from evidence-based learning of independence and decentralized schemes of local populations. The experimental results showed that the present proposal reduces the number of evaluations of the fitness function in the search for optimums, maintaining its effectiveness in comparison to other algorithms of state-of-art using the same benchmark of continuous functions. Keywords: Cellular EDA, learning, probabilistic graph model, Gaussian networks 1. Introduction Estimation of Distribution Algorithms (EDAs) [1, 2] have been widely used to find solutions in dis- crete [3] and continuous [4] optimization problems. These kinds of algorithms are based on substituting the crossover and mutation operators of the Genetic Algorithms (GAs) [5, 6] by the estimation and later sampling the probability distribution learned from the selected individuals. In every optimization problem, there are dependencies between the variables, which are not inferred by most of the current optimization methods (Genetic Algorithms, Particle Swarm Opti- mization, etc.). To detect the dependencies, EDAs use statistical techniques. The main advantage of ∗ Corresponding author. Ansel Y. Rodr´ ıguez-Gonz´ alez. E-mail: ansel@cicese.mx. EDAs over GAs is that they estimate the values of each variable using a probability distribution, while Genetic Algorithms seek a solution to a problem by directly coding the variables. For continuous optimization problems, several EDAs have been proposed: UMDA c [4], PBIL c [7], MIMIC c [8], EMNA (and its variants) [8–10] and PolyEDA [11]. However, a weakness of these algo- rithms, as well as the EDAs for discrete optimization, is the efficiency in terms of the number of evalua- tions of the objective function. In order to deal with this weakness for discrete optimization, a new kind of EDAs named the Cellular EDAs was proposed [12, 13], which allow for the decentralization of the individuals in the population. But in the best of our knowledge, this idea has not been used for continuous optimization problems. ISSN 1064-1246/19/$35.00 © 2019 – IOS Press and the authors. All rights reserved