Advances of evolutionary computation [electronic resource] : methods and operators / Erik Cuevas, Margarita Arimatea Díaz Cortés, Diego Alberto Oliva Navarro.
Cuevas, Erik, author.; Díaz Cortés, Margarita Arimatea, author.; Oliva Navarro, Diego Alberto, author.
2016
QA76.618
Available Online

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Title Advances of evolutionary computation [electronic resource] : methods and operators / Erik Cuevas, Margarita Arimatea Díaz Cortés, Diego Alberto Oliva Navarro.
ISBN 9783319285030 (electronic book)
3319285033 (electronic book)
9783319285023
DOI https://doi.org/10.1007/978-3-319-28503-0
Published Cham : Springer, 2016.
Language English
Description 1 online resource (xiv, 202 pages) : illustrations.
Other Standard Identifiers 10.1007/978-3-319-28503-0 doi
Call Number QA76.618
Dewey Decimal Classification 006.3/823
Summary 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.
Bibliography, etc. Note Includes bibliographical references.
Access Note Access limited to authorized users.
Source of Description Online resource; title from PDF title page (SpringerLink, viewed January 28, 2016).
Added Author Cuevas, Erik, author.
Díaz Cortés, Margarita Arimatea, author.
Oliva Navarro, Diego Alberto, author.
Series Studies in computational intelligence ; v. 629.
Available in Other Form Print version: 9783319285023
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Introduction
A Swarm Global Optimization Algorithm Inspired in the Behavior of the Social-spider.-A States of Matter Algorithm for Global Optimization
An Algorithm for Global Optimization Inspired by Collective Animal Behavior
A Bio-inspired Evolutionary Algorithm: Allostatic Optimization
Optimization Based on the Behavior of Locust Swarms.

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