Advances of Evolutionary Computation: Methods and Operators, 1st ed. 2016 Studies in Computational Intelligence Series, Vol. 629

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.

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.