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Multi-Objective Memetic Algorithms

  • Book
  • © 2009

Overview

Editors:
  1. Chi-Keong Goh

    1. Department of Electrical and Computer Engineering, National University of Singapore, Singapore

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    You can also search for this editor in PubMed   Google Scholar

  2. Yew-Soon Ong

    1. School of Computer Engineering, Nanyang Technological University, Singapore

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    You can also search for this editor in PubMed   Google Scholar

  3. Kay Chen Tan

    1. Department of Electrical and Computer Engineering, National University of Singapore, Singapore

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    You can also search for this editor in PubMed   Google Scholar

  • Recent research on Multi-objective Memetic Algorithms
  • Includes supplementary material: sn.pub/extras

Part of the book series: Studies in Computational Intelligence (SCI, volume 171)

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Table of contents (18 chapters)

  1. Front Matter

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  2. Introduction

    1. Front Matter

      Pages 1-1
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    2. Evolutionary Multi-Multi-Objective Optimization - EMMOO

      • Gideon Avigad
      Pages 3-26

    3. Implementation of Multiobjective Memetic Algorithms for Combinatorial Optimization Problems: A Knapsack Problem Case Study

      • Hisao Ishibuchi, Yasuhiro Hitotsuyanagi, Noritaka Tsukamoto, Yusuke Nojima
      Pages 27-49

  3. Knowledge Infused in Design of Problem-Specific Operators

    1. Front Matter

      Pages 51-51
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    2. Solving Time-Tabling Problems Using Evolutionary Algorithms and Heuristics Search

      • Dipti Srinivasan, Zhang Hua
      Pages 53-69

    3. An Efficient Genetic Algorithm with Uniform Crossover for the Multi-Objective Airport Gate Assignment Problem

      • Xiao-Bing Hu, Ezequiel Di Paolo
      Pages 71-89

    4. Application of Evolutionary Algorithms for Solving Multi-Objective Simulation Optimization Problems

      • Lee Loo Hay, Chew Ek Peng, Teng suyan, Li juxin
      Pages 91-110

    5. Feature Selection Using Single/Multi-Objective Memetic Frameworks

      • Zexuan Zhu, Yew-Soon Ong, Jer-Lai Kuo
      Pages 111-131

    6. Multi-Objective Robust Optimization Assisted by Response Surface Approximation and Visual Data-Mining

      • Koji Shimoyama, Jin Ne Lim, Shinkyu Jeong, Shigeru Obayashi, Masataka Koishi
      Pages 133-151

    7. Multiobjective Metamodel–Assisted Memetic Algorithms

      • Chariklia A. Georgopoulou, Kyriakos C. Giannakoglou
      Pages 153-181

    8. A Convergence Acceleration Technique for Multiobjective Optimisation

      • Salem F. Adra, Ian Griffin, Peter J. Fleming
      Pages 183-205

  5. Information Exploited for Local Improvement

    1. Front Matter

      Pages 279-279
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    2. Combination of Genetic Algorithms and Evolution Strategies with Self-adaptive Switching

      • Tatsuya Okabe, Yaochu Jin, Bernhard Sendhoff
      Pages 281-307

    3. Comparison between MOEA/D and NSGA-II on the Multi-Objective Travelling Salesman Problem

      • Wei Peng, Qingfu Zhang, Hui Li
      Pages 309-324

    4. Integrating Cross-Dominance Adaptation in Multi-Objective Memetic Algorithms

      • Andrea Caponio, Ferrante Neri
      Pages 325-351
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Keywords

About this book

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.

Editors and Affiliations

  • Department of Electrical and Computer Engineering, National University of Singapore, Singapore

    Chi-Keong Goh, Kay Chen Tan

  • School of Computer Engineering, Nanyang Technological University, Singapore

    Yew-Soon Ong

Bibliographic Information

  • Book Title: Multi-Objective Memetic Algorithms

  • Editors: Chi-Keong Goh, Yew-Soon Ong, Kay Chen Tan

  • Series Title: Studies in Computational Intelligence

  • DOI: https://doi.org/10.1007/978-3-540-88051-6

  • Publisher: Springer Berlin, Heidelberg

  • eBook Packages: Engineering, Engineering (R0)

  • Copyright Information: Springer-Verlag Berlin Heidelberg 2009

  • Hardcover ISBN: 978-3-540-88050-9Published: 26 February 2009

  • Softcover ISBN: 978-3-642-09978-6Published: 28 October 2010

  • eBook ISBN: 978-3-540-88051-6Published: 23 December 2008

  • Series ISSN: 1860-949X

  • Series E-ISSN: 1860-9503

  • Edition Number: 1

  • Number of Pages: XII, 404

  • Topics: Mathematical and Computational Engineering, Artificial Intelligence

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