Hybrid Evolutionary Algorithms

Crina Grosan, Ajith Abraham, Hisao Ishibuchi

Springer, Aug 29, 2007 - Computers - 404 pages

Hybridization of evolutionary algorithms is getting popular due to their capabilities in handling several real world problems involving complexity, noisy environment, imprecision, uncertainty and vagueness. This edited volume is targeted to present the latest state-of-the-art methodologies in “Hybrid Evolutionary Algorithms”. This book deals with the theoretical and methodological aspects, as well as various applications to many real world problems from science, technology, business or commerce. This volume comprises of 14 chapters including an introductory chapter giving the fundamental definitions and some important research challenges. Chapters were selected on the basis of fundamental ideas/concepts rather than the thoroughness of techniques deployed.

Contents

• Contents: 1
• QuantumInspired Evolutionary Algorithm: 18
• Enhanced Evolutionary Algorithms for Multidisciplinary Design: 39
• Hybrid Evolutionary Algorithms and Clustering Search: 77
• References: 98
• References: 124
• Particle Swarm Optimization Algorithm: 146
• References: 170
• AVR Using GABF Approach: 184
• Memetic Algorithms Parametric Optimization for Microlithography: 201
• References: 238
• A Hybrid Evolutionary Heuristic for Job Scheduling: 269
• A Hybrid Approach: 313
• Robust Parametric Image Registration: 336
• Pareto Evolutionary Algorithm Hybridized with Local Search: 361
• References: 394

Popular passages

Page 2 - They all share a common conceptual base of simulating the evolution of Individual structures via processes of Selection, Mutation, and Reproduction.‎

Page 1 - The evolutionary algorithm can be applied to problems where heuristic solutions are not available or generally lead to unsatisfactory results. As a result, evolutionary...‎

Page 5 - The integration of different learning and adaptation techniques, to overcome individual limitations and achieve synergetic effects through hybridization or fusion of these techniques, has in recent years contributed to a large number of new hybrid evolutionary systems.‎

Bibliographic information

• Title: Hybrid Evolutionary Algorithms; Volume 75 of Studies in Computational Intelligence
• Editors: Crina Grosan, Ajith Abraham, Hisao Ishibuchi
• Edition: illustrated
• Publisher: Springer, 2007
• ISBN: 3540732977, 9783540732976
• Length: 404 pages
• Subjects: Computers / Artificial Intelligence / General; Computers / Design, Graphics & Media / CAD-CAM; Computers / Software Development & Engineering / General; Mathematics / Applied; Technology & Engineering / Engineering (General)