Evolutionary Computing

1. Front Matter

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2. Formal memetic algorithms

   • Nicholas J. Radcliffe, Patrick D. Surry

   Pages 1-16

3. A statistical mechanical formulation of the dynamics of genetic algorithms

   • Jonathan Shapiro, Adam Prügel-Bennett, Magnus Rattray

   Pages 17-27

4. Evolutionary stability in simple classifier systems

   • D. F. Yates, A. Fairley

   Pages 28-37

5. Nonbinary transforms for genetic algorithm problems

   • Paul Field

   Pages 38-50

6. Enhancing evolutionary computation using analogues of biological mechanisms

   • Ray Paton

   Pages 51-64

7. Exploiting mate choice in evolutionary computation: Sexual selection as a process of search, optimization, and diversification

   • Geoffrey F. Miller

   Pages 65-79

8. An empirical comparison of selection methods in evolutionary algorithms

   • Peter J. B. Hancock

   Pages 80-94

9. An evolution strategy and genetic algorithm hybrid: An initial implementation and first results

   • Lawrence Bull, Terence C Fogarty

   Pages 95-102

10. Genetic algorithms and directed adaptation

    • John Coyne, Ray Paton

    Pages 103-114

11. Genetic algorithms and neighbourhood search

    • Colin R. Reeves

    Pages 115-130

12. A unified paradigm for parallel Genetic Algorithms

    • A. Kapsalis, G. D. Smith, V. J. Rayward-Smith

    Pages 131-149

13. Distributed coevolutionary genetic algorithms for multi-criteria and multi-constraint optimisation

    • Phil Husbands

    Pages 150-165

14. Inductive operators and rule repair in a hybrid genetic learning system: Some initial results

    • A. Fairley, D. F. Yates

    Pages 166-179

15. Adaptive learning of a robot arm

    • Mukesh J. Patel, Marco Dorigo

    Pages 180-194

16. Co-evolving Co-operative populations of rules in learning control systems

    • Terence C. Fogarty

    Pages 195-209

17. Learning anticipatory behaviour using a delayed action classifier system

    • Brian Carse

    Pages 210-223

18. Applying a restricted mating policy to determine state space niches using immediate and delayed reinforcement

    • Chris Melhuish, Terence C. Fogarty

    Pages 224-237

19. A comparison between two architectures for searching and learning in maze problems

    • A. G. Pipe, B. Carse

    Pages 238-249

20. Fast practical evolutionary timetabling

    • Dave Corne, Peter Ross, Hsiao-Lan Fang

    Pages 250-263

This volume is based on the Workshop on Evolutionary Computing held in Leeds, U.K. in April 1994 under the sponsorship of the Society for the Study of Artificial Intelligence and Simulation of Behaviour. In addition to the 22 best papers presented at the workshop, there are two invited contributions by Ray Paton and Colin Reever.
The volume addresses several aspects of evolutionary computing, particularly genetic algorithms, and its applications, for example in search, robotics, signal processing, machine learning, and scheduling. The papers are organized in sections on theoretical and biological foundations, techniques, classifier systems, and applications.

• algorithm
• algorithms
• artificial intelligence
• evolution
• evolutionary computation
• genetic algorithm
• genetic algorithms
• intelligence
• learning
• machine learning
• robot
• robotics
• simulation
• algorithm analysis and problem complexity

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