Advances Of Evolutionary Computation: Methods And Operators (studies In Computational Intelligence)

"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."-- Unedited summary from book "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" -- OhioLink Library Catalog Front Matter....Pages i-xiv Introduction....Pages 1-8 A Swarm Global Optimization Algorithm Inspired in the Behavior of the Social-Spider....Pages 9-33 A States of Matter Algorithm for Global Optimization....Pages 35-54 The Collective Animal Behavior method....Pages 55-81 An Evolutionary Computation Algorithm based on the Allostatic Optimization....Pages 83-100 Optimization Based on the Behavior of Locust Swarms....Pages 101-120 Reduction of Function Evaluations by using an evolutionary computation algorithm....Pages 121-152 Collective Animal Behavior Algorithm for Multimodal Optimization Functions....Pages 153-174 Social-Spider Algorithm for Constrained Optimization....Pages 175-202