Hybrid Evolutionary Algorithms

Herausgegeben:Grosan, Crina; Abraham, Ajith; Ishibuchi, Hisao

• Broschiertes Buch

Produktbeschreibung

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.

Produktdetails

• Studies in Computational Intelligence 75
• Verlag: Springer / Springer Berlin Heidelberg / Springer, Berlin
• Artikelnr. des Verlages: 978-3-642-09235-0
• Softcover reprint of hardcover 1st ed. 2007
• Seitenzahl: 420
• Erscheinungstermin: 16. November 2010
• Englisch
• Abmessung: 235mm x 155mm x 23mm
• Gewicht: 634g
• ISBN-13: 9783642092350
• ISBN-10: 3642092357
• Artikelnr.: 32014448

Autorenporträt

Dr. Ajith Abraham is Director of the Machine Intelligence Research (MIR) Labs, a global network of research laboratories with headquarters near Seattle, WA, USA. He is an author/co-author of more than 750 scientific publications. He is founding Chair of the International Conference of Computational Aspects of Social Networks (CASoN), Chair of IEEE Systems Man and Cybernetics Society Technical Committee on Soft Computing (since 2008), and a Distinguished Lecturer of the IEEE Computer Society representing Europe (since 2011).

Inhaltsangabe

Hybrid Evolutionary Algorithms: Methodologies, Architectures, and Reviews.- Quantum-Inspired Evolutionary Algorithm for Numerical Optimization.- Enhanced Evolutionary Algorithms for Multidisciplinary Design Optimization: A Control Engineering Perspective.- Hybrid Evolutionary Algorithms and Clustering Search.- A Novel Hybrid Algorithm for Function Optimization: Particle Swarm Assisted Incremental Evolution Strategy.- An Efficient Nearest Neighbor Classifier.- Hybrid Genetic: Particle Swarm Optimization Algorithm.- A Hybrid Genetic Algorithm and Bacterial Foraging Approach for Global Optimization and Robust Tuning of PID Controller with Disturbance Rejection.- Memetic Algorithms Parametric Optimization for Microlithography.- Significance of Hybrid Evolutionary Computation for Ab Initio Protein Folding Prediction.- A Hybrid Evolutionary Heuristic for Job Scheduling on Computational Grids.- Clustering Gene-Expression Data: A Hybrid Approach that Iterates Between k-Means and Evolutionary Search.- Robust Parametric Image Registration.- Pareto Evolutionary Algorithm Hybridized with Local Search for Biobjective TSP.