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.
Table of 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.