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The goal of this book is topresent advances that discuss alternative Evolutionary Computation (EC) developmentsand non-conventional operators which have proved to be e ective in the solutionof several complex problems. The book has been structured so that each chaptercan be read independently from the others. The book contains nine chapters withthe following themes: 1) Introduction, 2) the Social Spider Optimization (SSO),3) the States of Matter Search (SMS), 4) the collective animal behavior (CAB)algorithm, 5) the Allostatic Optimization (AO) method, 6) the Locust Search(LS) algorithm, 7) the Adaptive Population with Reduced Evaluations (APRE)method, 8) the multimodal CAB, 9) the constrained SSO method.
Table des matières
Introduction.- A Swarm Global Optimization Algorithm Inspired in the Behavior of the Social-spider.-A States ofMatter Algorithm for Global Optimization.- An Algorithm for Global Optimization Inspiredby Collective Animal Behavior.- A Bio-inspired Evolutionary Algorithm: AllostaticOptimization.- Optimization Based on the Behavior of Locust Swarms.
A propos de l'auteur
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. 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.
Daniel Zaldivar graduated from the University of Guadalajara, Mexico in 1995 with a B.S. degree in Electronics and Communications Engineering. Later, in 2000, he earned his M.Sc. degree in Industrial Electronics from ITESO, Mexico, and in 2006 he received his Ph.D. degree from Freie Universität Berlin, Germany. Since then, he has been employed as a full-time Professor in the Department of Computer Science at the University of Guadalajara, where he currently holds his position.
Ernesto Ayala, originally from León, Guanajuato was born in 1982. He received the title of Electrical Mechanical Engineer in 2017 and in 2019 the master's degree in Applied Computing at the University of Guadalajara. He is currently a PhD candidate in Electronics and Computing Sciences. Since 2018, he has been teaching curricular courses in Robotics Engineering and Electronic Engineering in the Division of Technologies for Cyber-human Integration of the University Center for Exact Sciences and Engineering. His area of expertise is computer vision and evolutionary computing.
Mr. Ayala collaborates with a research group atthe University of Guadalajara focused on the development of ecological and autonomous driving vehicles.
Oscar González received his B.S. with distinction in Electronic Engineering and Communications from the University of Guadalajara, Mexico, in 2022. During the COVID-19 pandemic, he was a member of the advisory committee for the COVID-19 pandemic of the University of Guadalajara. For his contributions and studies on COVID-19, he has been awarded the Irene Robledo García Award, the highest distinction of the University of Guadalajara for social service in 2022.
Fernando Vega received the title of technical career in electricity by C.B.E.T.I.S. in 2014. Obtained a B.S. degree in Mechatronics from the National Technologist of Mexico, campus Culiacan, Mexico, in 2019. He is part of the University of Guadalajara, a full-time student M.S. in the Electronics and Computer Science program. His current research interests include motors design, electric vehicle design, Metaheuristics.
Résumé
The goal of this book is to
present advances that discuss alternative Evolutionary Computation (EC) developments
and non-conventional operators which have proved to be effective in the solution
of several complex problems. The book has been structured so that each chapter
can be read independently from the others. The book contains nine chapters with
the following themes: 1) Introduction, 2) the Social Spider Optimization (SSO),
3) the States of Matter Search (SMS), 4) the collective animal behavior (CAB)
algorithm, 5) the Allostatic Optimization (AO) method, 6) the Locust Search
(LS) algorithm, 7) the Adaptive Population with Reduced Evaluations (APRE)
method, 8) the multimodal CAB, 9) the constrained SSO method.
