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Advances of Evolutionary Computation: Methods and Operators / by Erik Cuevas, Margarita Arimatea Díaz Cortés, Diego Alberto Oliva Navarro

By: Cuevas, Erik.
Contributor(s): Díaz Cortés, Margarita Arimatea | Oliva Navarro, Diego Alberto. | SpringerLink (Online service)
Material type: materialTypeLabelE-bookSeries: (Studies in Computational Intelligence, 1860-949X; 629).Publisher: Cham : Springer International Publishing, 2016Edition: 1st ed.Description: 1 recurso en línea (XIV, 202 páginas) : 48 ilustraciones, 43 ilustraciones en color.ISBN: 9783319285030.Subject: Inteligencia artificial | Computación evolutivaDDC classification: 006.3 Online resources: Acceso a este recurso digital (usuarios Universidad Europea de Madrid)Digital Resources
Contents:
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. .
Abstract: 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 e?ective 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.
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Holdings
Item type Current library Collection Call number Copy number Status Date due Barcode Item holds
LIBRO-E NO PRÉSTAMO LIBRO-E NO PRÉSTAMO Madrid Digital Acceso Electrónico (UEM) Ciencias e Ingeniería Q342 .C848 2016 EB (Browse shelf(Opens below)) .i11590981 Acceso electrónico eBOOK .i11590981
Total holds: 0

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. .

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 e?ective 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.

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