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Knowledge Incorporation in Evolutionary Computation

  • Book
  • © 2005

Overview

Editors:
  1. Yaochu Jin

    1. Honda Research Institute Europe GmbH, Offenbach/Main, Germany

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  • State of the art in knowledge incorporation in evolutionary computation
  • Comprehensive and self-contained
  • Includes a concise introduction to evolutionary algorithms as well as knowledge representation methods
  • Includes supplementary material: sn.pub/extras

Part of the book series: Studies in Fuzziness and Soft Computing (STUDFUZZ, volume 167)

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Table of contents (24 chapters)

  1. Front Matter

    Pages I-XIII
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  2. Introduction

    1. Front Matter

      Pages 1-1
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    2. A Selected Introduction to Evolutionary Computation

      • Xin Yao
      Pages 3-12

  3. Knowledge Incorporation in Initialization, Recombination and Mutation

    1. Front Matter

      Pages 13-13
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    2. The Use of Collective Memory in Genetic Programming

      • Keith Bearpark, Andy J. Keane
      Pages 15-36

    3. A Cultural Algorithm for Solving the Job Shop Scheduling Problem

      • Ricardo Landa Becerra, Carlos A. Coello Coello
      Pages 37-55

    4. Case-Initialized Genetic Algorithms for Knowledge Extraction and Incorporation

      • Judy Johnson, Sushil J. Louis
      Pages 57-79

    5. Using Cultural Algorithms to Evolve Strategies in A Complex Agent-based System

      • David A. Ostrowski, Robert G. Reynolds
      Pages 81-102

    6. Methods for Using Surrogate Models to Speed Up Genetic Algorithm Optimization: Informed Operators and Genetic Engineering

      • Khaled Rasheed, Xiao Ni, Swaroop Vattam
      Pages 103-122

    7. Fuzzy Knowledge Incorporation in Crossover and Mutation

      • Jun Zhang, Henry S. H. Chung, Alan W. L. Lo, B. J. Hu
      Pages 123-143

  4. Knowledge Incorporation in Selection and Reproduction

    1. Front Matter

      Pages 145-145
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    2. Learning Probabilistic Models for Enhanced Evolutionary Computation

      • Peter A. N. Bosman, Dirk Thierens
      Pages 147-176

    3. Probabilistic Models for Linkage Learning in Forest Management

      • Els I. Ducheyne, B. De Baets, R. De Wulf
      Pages 177-194

    4. Performance-Based Computation of Chromosome Lifetimes in Genetic Algorithms

      • Adnan Acan, Yüce Tekol
      Pages 195-214

    5. Genetic Algorithm and Case-Based Reasoning Applied in Production Scheduling

      • Pei-Chann Chang, Jih-Chang Hsieh, Yen-Wen Wang
      Pages 215-236

    6. Knowledge-Based Evolutionary Search for Inductive Concept Learning

      • Federico Divina, Elena Marchiori
      Pages 237-253

    7. An Evolutionary Algorithm with Tabu Restriction and Heuristic Reasoning for Multiobjective Optimization

      • E. F. Khor, K. C. Tan, Y. J. Yang
      Pages 255-277

  5. Knowledge Incorporation in Fitness Evaluations

    1. Front Matter

      Pages 279-279
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    2. Neural Networks for Fitness Approximation in Evolutionary Optimization

      • Yaochu Jin, Michael Hüsken, Markus Olhofer, Bernhard Sendhoff
      Pages 281-306

    3. Surrogate-Assisted Evolutionary Optimization Frameworks for High-Fidelity Engineering Design Problems

      • Yew Soon Ong, P. B. Nair, A. J. Keane, K. W. Wong
      Pages 307-331
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Keywords

About this book

Incorporation of a priori knowledge, such as expert knowledge, meta-heuristics and human preferences, as well as domain knowledge acquired during evolu­ tionary search, into evolutionary algorithms has received increasing interest in the recent years. It has been shown from various motivations that knowl­ edge incorporation into evolutionary search is able to significantly improve search efficiency. However, results on knowledge incorporation in evolution­ ary computation have been scattered in a wide range of research areas and a systematic handling of this important topic in evolutionary computation still lacks. This edited book is a first attempt to put together the state-of-art and re­ cent advances on knowledge incorporation in evolutionary computation within a unified framework. Existing methods for knowledge incorporation are di­ vided into the following five categories according to the functionality of the incorporated knowledge in the evolutionary algorithms. 1. Knowledge incorporation in representation, population initialization, - combination and mutation. 2. Knowledge incorporation in selection and reproduction. 3. Knowledge incorporation in fitness evaluations. 4. Knowledge incorporation through life-time learning and human-computer interactions. 5. Incorporation of human preferences in multi-objective evolutionary com­ putation. The intended readers of this book are graduate students, researchers and practitioners in all fields of science and engineering who are interested in evolutionary computation. The book is divided into six parts. Part I contains one introductory chapter titled "A selected introduction to evolutionary computation" by Yao, which presents a concise but insightful introduction to evolutionary computation.

Editors and Affiliations

  • Honda Research Institute Europe GmbH, Offenbach/Main, Germany

    Yaochu Jin

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