Multi-Objective Memetic Algorithms: Studies in Computational Intelligence, cartea 171
Editat de Chi-Keong Goh, Yew-Soon Ong, Kay Chen Tanen Limba Engleză Paperback – 28 oct 2010
This book presents a very first comprehensive collection of works, written by leading researchers in the field, and reflects the current state-of-the-art in the theory and practice of multi-objective Memetic algorithms. "Multi-Objective Memetic algorithms" is organized for a wide readership and will be a valuable reference for engineers, researchers, senior undergraduates and graduate students who are interested in the areas of Memetic algorithms and multi-objective optimization.
Toate formatele și edițiile | Preț | Express |
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Paperback (1) | 930.96 lei 43-57 zile | |
Springer Berlin, Heidelberg – 28 oct 2010 | 930.96 lei 43-57 zile | |
Hardback (1) | 936.95 lei 43-57 zile | |
Springer Berlin, Heidelberg – 26 feb 2009 | 936.95 lei 43-57 zile |
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Specificații
ISBN-13: 9783642099786
ISBN-10: 3642099785
Pagini: 416
Ilustrații: XII, 404 p.
Dimensiuni: 155 x 235 x 22 mm
Greutate: 0.58 kg
Ediția:Softcover reprint of hardcover 1st ed. 2009
Editura: Springer Berlin, Heidelberg
Colecția Springer
Seria Studies in Computational Intelligence
Locul publicării:Berlin, Heidelberg, Germany
ISBN-10: 3642099785
Pagini: 416
Ilustrații: XII, 404 p.
Dimensiuni: 155 x 235 x 22 mm
Greutate: 0.58 kg
Ediția:Softcover reprint of hardcover 1st ed. 2009
Editura: Springer Berlin, Heidelberg
Colecția Springer
Seria Studies in Computational Intelligence
Locul publicării:Berlin, Heidelberg, Germany
Public țintă
ResearchCuprins
Evolutionary Multi-Multi-Objective Optimization - EMMOO.- Implementation of Multiobjective Memetic Algorithms for Combinatorial Optimization Problems: A Knapsack Problem Case Study.- Knowledge Infused in Design of Problem-Specific Operators.- Solving Time-Tabling Problems Using Evolutionary Algorithms and Heuristics Search.- An Efficient Genetic Algorithm with Uniform Crossover for the Multi-Objective Airport Gate Assignment Problem.- Application of Evolutionary Algorithms for Solving Multi-Objective Simulation Optimization Problems.- Feature Selection Using Single/Multi-Objective Memetic Frameworks.- Multi-Objective Robust Optimization Assisted by Response Surface Approximation and Visual Data-Mining.- Multiobjective Metamodel–Assisted Memetic Algorithms.- A Convergence Acceleration Technique for Multiobjective Optimisation.- Knowledge Propagation through Cultural Evolution.- Risk and Cost Tradeoff in Economic Dispatch Including Wind Power Penetration Based on Multi-Objective Memetic Particle Swarm Optimization.- Hybrid Behavioral-Based Multiobjective Space Trajectory Optimization.- Nature-Inspired Particle Mechanics Algorithm for Multi-Objective Optimization.- Information Exploited for Local Improvement.- Combination of Genetic Algorithms and Evolution Strategies with Self-adaptive Switching.- Comparison between MOEA/D and NSGA-II on the Multi-Objective Travelling Salesman Problem.- Integrating Cross-Dominance Adaptation in Multi-Objective Memetic Algorithms.- A Memetic Algorithm for Dynamic Multiobjective Optimization.- A Memetic Coevolutionary Multi-Objective Differential Evolution Algorithm.- Multiobjective Memetic Algorithm and Its Application in Robust Airfoil Shape Optimization.
Textul de pe ultima copertă
The application of sophisticated evolutionary computing approaches for solving complex problems with multiple conflicting objectives in science and engineering have increased steadily in the recent years. Within this growing trend, Memetic algorithms are, perhaps, one of the most successful stories, having demonstrated better efficacy in dealing with multi-objective problems as compared to its conventional counterparts. Nonetheless, researchers are only beginning to realize the vast potential of multi-objective Memetic algorithm and there remain many open topics in its design.
This book presents a very first comprehensive collection of works, written by leading researchers in the field, and reflects the current state-of-the-art in the theory and practice of multi-objective Memetic algorithms. "Multi-Objective Memetic algorithms" is organized for a wide readership and will be a valuable reference for engineers, researchers, senior undergraduates and graduate students who are interested in the areas of Memetic algorithms and multi-objective optimization.
This book presents a very first comprehensive collection of works, written by leading researchers in the field, and reflects the current state-of-the-art in the theory and practice of multi-objective Memetic algorithms. "Multi-Objective Memetic algorithms" is organized for a wide readership and will be a valuable reference for engineers, researchers, senior undergraduates and graduate students who are interested in the areas of Memetic algorithms and multi-objective optimization.
Caracteristici
Recent research on Multi-objective Memetic Algorithms Includes supplementary material: sn.pub/extras