Multi-Objective Memetic Algorithms / Edition 1

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More About This Textbook

Overview

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.

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Product Details

  • ISBN-13: 9783540880509
  • Publisher: Springer Berlin Heidelberg
  • Publication date: 2/26/2009
  • Series: Studies in Computational Intelligence Series , #171
  • Edition description: 2009
  • Edition number: 1
  • Pages: 404
  • Product dimensions: 9.21 (w) x 6.14 (h) x 0.94 (d)

Table of Contents

Part I Introduction

Evolutionary Multi-Multi-Objective Optimization - EMMOO Gideon Avigad 3

Implementation of Multiobjective Memetic Algorithms for Combinatorial Optimization Problems: A Knapsack Problem Case Study Hisao Ishibuchi Yasuhiro Hitotsuyanagi Noritaka Tsukamoto Yusuke Nojima 27

Part II Knowledge Infused in Design of Problem-Specific Operators

Solving Time-Tabling Problems Using Evolutionary Algorithms and Heuristics Search Dipti Srinivasan Zhang Hua 53

An Efficient Genetic Algorithm with Uniform Crossover for the Multi-Objective Airport Gate Assignment Problem Xiao-Bing Hu Ezequiel Di Paolo 71

Application of Evolutionary Algorithms for Solving Multi-Objective Simulation Optimization Problems Lee Loo Hay Chew Ek Peng Teng Suyan Li Juxin 91

Feature Selection Using Single/Multi-Objective Memetic Frameworks Zexuan Zhu Yew-Soon Ong Jer-Lai Kuo 111

Multi-Objective Robust Optimization Assisted by Response Surface Approximation and Visual Data-Mining Koji Shimoyama Jin Ne Lim Shinkyu Jeong Shigeru Obayashi Masataka Koishi 133

Multiobjective Metamodel-Assisted Memetic Algorithms Chariklia A. Georgopoulou Kyriakos C. Giannakoglou 153

A Convergence Acceleration Technique for Multiobjective Optimisation Salem F. Adra Ian Griffin Peter J. Fleming 183

Part III Knowledge Propagation through Cultural Evolution

Risk and Cost Tradeoff in Economic Dispatch Including Wind Power Penetration Based on Multi-Objective Memetic Particle Swarm Optimization Lingfeng Wang Chanan Singh 209

Hybrid Behavioral-Based Multiobjective Space Trajectory Optimization Massimiliano Vasile 231

Nature-Inspired Particle Mechanics Algorithm for Multi-Objective Optimization Xiang Feng FrancisC.M. Lau 255

Part IV Information Exploited for Local Improvement

Combination of Genetic Algorithms and Evolution Strategies with Self-adaptive Switching Tatsuya Okabe Yaochu Jin Bernhard Sendhoff 281

Comparison between MOEA/D and NSGA-II on the Multi-Objective Travelling Salesman Problem Wei Peng Qingfu Zhang Hui Li 309

Integrating Cross-Dominance Adaptation in Multi-Objective Memetic Algorithms Andrea Caponio Ferrante Neri 325

A Memetic Algorithm for Dynamic Multiobjective Optimization Tapabrata Ray Amitay Isaacs Warren Smith 353

A Memetic Coevolutionary Multi-Objective Differential Evolution Algorithm Omar Soliman Lam T. Bui Hussein Abbass 369

Multiobjective Memetic Algorithm and Its Application in Robust Airfoil Shape Optimization Wenbin Song 389

Author Index 403

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