Not all PBILs are the same: Unveiling the different learning mechanisms of PBIL variants |
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| Affiliation: | 1. DInf – Federal University of Parana, CP: 19081, CEP 19031-970, Curitiba, Brazil;2. Intelligent System Group, University of the Basque Country, San Sebastian, Spain;1. Division of Computational Mechatronics, Institute for Computational Science, Ton Duc Thang University, Ho Chi Minh City, Vietnam;2. Faculty of Electrical and Electronics Engineering, Ton Duc Thang University, Ho Chi Minh City, Vietnam;3. Vietnam-German University (VGU), Vietnam;4. Department of Mechanical Engineering, Incheon National University, South Korea;1. Technical Engineering Department, University of Mohaghegh Ardabili, Ardabil, Iran;2. Systems Engineering Group, Department of Engineering & Technology, University of Huddersfield, UK;1. Dept. of Computer Eng. & Systems, Faculty of Engineering, Mansoura University, Mansoura, Egypt;2. Dept. of Computer Science, Faculty of Computers, Mansoura University, Mansoura, Egypt;1. Institute of Computer Science, Siedlce University of Natural Sciences and Humanities, 3-Maja 54, 08-110 Siedlce, Poland;2. Institute of Computer Science, Polish Academy of Sciences, Jana Kazimierza 5, 01-248 Warsaw, Poland;3. Faculty of Mathematics and Computer Science, Lodz University, Banacha 22, 90-238 Lodz, Poland;1. Departamento de Informática y Estadística, Universidad Rey Juan Carlos, Móstoles, Spain;2. Departamento Estadística e Investigación Operativa, Universidad de Valencia, Burjassot, Spain;3. Lab-STICC, Centre de Recherche, Université de Bretagne-Sud, Lorient, France;4. LERIA, Département d’informatique, Université d’Angers, Angers, France;1. Department of Computer and Information Science, The Norwegian University of Science and Technology, Norway;2. Department of Computer Science, Aalborg University, Denmark;3. Department of Mathematics, University of Almería, Spain;4. HUGIN EXPERT A/S, Aalborg, Denmark |
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| Abstract: | Model-based optimization using probabilistic modeling of the search space is one of the areas where research on evolutionary algorithms (EAs) has considerably advanced in recent years. The population-based incremental algorithm (PBIL) is one of the first algorithms of its kind and it has been extensively applied to many optimization problems. In this paper we show that the different applications of PBIL reported in the literature correspond, in fact, to two essentially different algorithms, which are defined by the way the learning step is implemented. We analytically and empirically study the impact of the learning method on the search behavior of the algorithm. As a result of our research, we show examples in which the choice of a PBIL variant can produce qualitatively different outputs of the search process. |
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| Keywords: | Probabilistic modeling PBIL Estimation of distribution algorithm |
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