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This book examines a series of strategies designed to enhance metaheuristic algorithms, focusing on critical aspects such as initialization methods, the incorporation of Evolutionary Game Theory to develop novel search mechanisms, and the application of learning concepts to refine evolutionary operators. Furthermore, it emphasizes the significance of diversity and opposition in preventing premature convergence and improving algorithmic efficiency. These strategies collectively contribute to the development of more adaptive and robust optimization techniques. The book was designed from a teaching standpoint, making it suitable for undergraduate and postgraduate students in Science, Electrical Engineering, or Computational Mathematics. Furthermore, engineering practitioners unfamiliar with metaheuristic computations will find value in the application of these techniques to address complex real-world engineering problems, extending beyond theoretical constructs.
About the author
Dr. 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.
Alma Rodriguez earned her Bachelor of Science in Industrial Engineering and her Master's degree from CETI, Mexico, in 2005 and 2007, respectively. She went on to achieve her Doctorate in Engineering from the Universidad de Guadalajara, located in Guadalajara, Mexico, in 2021. Dr. Rodriguez has made her mark as an author of numerous engineering-related scientific publications. She contributed as a co-author to the publication "Recent Metaheuristic Computation Schemes in Engineering," released by Springer International Publishing. Her research primarily focuses on the areas of Metaheuristic Algorithms, Supplier Selection, Inventory Theory, and the broader field of optimization.Beatriz Rivera received a B.S. degree with distinction in Computer Engineering from UNIVA, México, a M.Sc. degree in Engineering Systems from UANL, México. Since 2014, she has been with The University of Guadalajara, where she is currently a Professor and enrolled in the Ph.D. program in Electronics and Computer Science. Her current research interests are metaheuristic algorithms and artificial intelligence.
Jesús López obtained a bachelor's degree in Communications and Electronics Engineering in 2009 and a Master of Science degree in Electronic and Computer Engineering in 2014 from Centro Universitario de Ciencias Exactas e Ingenierías (CUCEI) of the University of Guadalajara, Mexico. He is currently pursuing a Ph. D. in Science degree in Electronic and Computer Engineering from 2021 at the University of Guadalajara. Collaborator in the development of two patents: "Magnetic levitator system for balancing a biped robot" and "Variable transmission system based on gear assemblies forming a truncated sphere". His research interests include metaheuristics algorithms, artificial intelligence, robotics topics, artificial vision, and their applications.
Carlos Guzmán received the bachelor's degree in Mechatronics Engineering from Universidad Politécnica de Sinaloa, Mexico in 2020 and a M.Sc. degree in Electronic and Computer Engineering in 2023 from the University of Guadalajara, Mexico. He is currently pursuing a Ph.D degree in Electronics and Computer Science at the University of Guadalajara, Mexico. His research interests include artificial vision and their applications.
Summary
This book examines a series of strategies designed to enhance metaheuristic algorithms, focusing on critical aspects such as initialization methods, the incorporation of Evolutionary Game Theory to develop novel search mechanisms, and the application of learning concepts to refine evolutionary operators. Furthermore, it emphasizes the significance of diversity and opposition in preventing premature convergence and improving algorithmic efficiency. These strategies collectively contribute to the development of more adaptive and robust optimization techniques. The book was designed from a teaching standpoint, making it suitable for undergraduate and postgraduate students in Science, Electrical Engineering, or Computational Mathematics. Furthermore, engineering practitioners unfamiliar with metaheuristic computations will find value in the application of these techniques to address complex real-world engineering problems, extending beyond theoretical constructs.
