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This book is primarily intended for undergraduate and postgraduate students of Science, Electrical Engineering, or Computational Mathematics. Metaheuristic search methods are so numerous and varied in terms of design and potential applications; however, for such an abundant family of optimization techniques, there seems to be a question which needs to be answered: Which part of the design in a metaheuristic algorithm contributes more to its better performance? Several works that compare the performance among metaheuristic approaches have been reported in the literature. Nevertheless, they…mehr

Produktbeschreibung
This book is primarily intended for undergraduate and postgraduate students of Science, Electrical Engineering, or Computational Mathematics. Metaheuristic search methods are so numerous and varied in terms of design and potential applications; however, for such an abundant family of optimization techniques, there seems to be a question which needs to be answered: Which part of the design in a metaheuristic algorithm contributes more to its better performance? Several works that compare the performance among metaheuristic approaches have been reported in the literature. Nevertheless, they suffer from one of the following limitations: (A)Their conclusions are based on the performance of popular evolutionary approaches over a set of synthetic functions with exact solutions and well-known behaviors, without considering the application context or including recent developments. (B) Their conclusions consider only the comparison of their final results which cannot evaluate the nature of a good or bad balance between exploration and exploitation. The objective of this book is to compare the performance of various metaheuristic techniques when they are faced with complex optimization problems extracted from different engineering domains. The material has been compiled from a teaching perspective.


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Autorenporträt
Erik Cuevas received his B.S. degree with distinction in Electronics and Communications Engineering from the University of Guadalajara, Mexico, in 1995, the M.Sc. degree in Industrial Electronics from ITESO, Mexico, in 2000, and the Ph.D. degree from Freie Universität Berlin, Germany in 2006. Since 2006 he has been with the University of Guadalajara, where he is currently a full-time Professor in the Department of Computer Science. Since 2008, he is a member of the Mexican National Research System (SNI III). He is the author of several books and articles. A list of his books and publications can be seen in the CV attached to this application. His current research interest includes Meta-heuristics, computer vision, and mathematical methods. He serves as an editor in Expert System with Applications, ISA Transactions, and Applied Soft Computing, Applied Mathematical Modeling and Mathematics and Computers in Simulation. Alberto Luque Chang graduated with a Bachelor's Degree in Communications and Electronics Engineering (2013), a Master of Science in Electronic Engineering and Computing (2016), and a Doctorate in Electronics and Computing Sciences (2021) in the University of Guadalajara (UdeG). He is currently a professor in the Division of Technologies for Cyber-Human Integration at the University Center for Exact Sciences and Engineering (CUCEI) of the UdeG. Likewise, since 2021, Dr. Luque is a member of the National System of Researchers, having the distinction of National Researcher Level 1. His areas of interest in research are Metaheuristic Algorithms, Artificial Intelligence, Optimization, Machine Learning and its applications. to Image Processing. Héctor Escobar received a B.S. degree with honors in Information Systems Engineering from the Autonomous University of Sinaloa, Mexico, in 2018 and an M.S. degree in Electronics and Computer Engineering from the University of Guadalajara, Mexico, in 2021. He is part of the Universityof Guadalajara, where he is a full-time Ph.D. student in the Electronics and Computer Science program. His current research interests include Metaheuristics, computer vision, artificial intelligence, and Agent-Based Modeling.