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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. Information Exploited for Local Improvement

    1. A Memetic Algorithm for Dynamic Multiobjective Optimization

      • Tapabrata Ray, Amitay Isaacs, Warren Smith
      Pages 353-367

    2. A Memetic Coevolutionary Multi-Objective Differential Evolution Algorithm

      • Omar Soliman, Lam T. Bui, Hussein Abbass
      Pages 369-388

    3. Multiobjective Memetic Algorithm and Its Application in Robust Airfoil Shape Optimization

      • Wenbin Song
      Pages 389-402

  2. Back Matter

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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

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