Knowledge Incorporation in Evolutionary Computation

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

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.

• Case-Based Reasoning
• Computer
• Evolution Strategies
• Evolutionary Computation
• Fuzzy
• Fuzzy Systems
• Genetic Algorithms
• Genetic programming
• Knowledge Incorporation
• Knowledge Representations
• human-computer interaction
• intelligence
• knowledge representation
• optimization
• programming

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

  Yaochu Jin

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