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ORCIDEvolutionary Computing, AISB Workshop 1994: Leeds, UK
- Terence C. Fogarty:
Evolutionary Computing, AISB Workshop, Leeds, UK, April 11-13, 1994, Selected Papers. Lecture Notes in Computer Science 865, Springer 1994, ISBN 3-540-58483-8
Theoretical and Biological Foundations
- Nicholas J. Radcliffe, Patrick D. Surry:
Formal Memetic Algorithms. 1-16 - Jonathan Shapiro, Adam Prügel-Bennett, Magnus Rattray:
A Statistical Mechanical Formulation of the Dynamics of Genetic Algorithms. 17-27 - Derek F. Yates, Andrew Fairley:
Evolutionary Stability in Simple Classifier Systems. 28-37 - Paul Field:
Nonbinary Transforms for Genetic Algorithm Problems. 38-50 - Ray Paton:
Enhancing Evolutionary Computation Using Analogues of Biological Mechanisms. 51-64 - Geoffrey F. Miller:
Exploiting Mate Choice in Evolutionary Computation: Sexual Selection as a Process of Search, Optimization, And Diversification. 65-79
Techniques
- Peter J. B. Hancock:
An Empirical Comparison of Selection Methods in Evolutionary Algorithms. 80-94 - Larry Bull, Terence C. Fogarty:
An Evolution Strategy and Genetic Algorithm Hybrid: An Initial Implementation and First Results. 95-102 - John Coyne, Ray Paton:
Genetic Algorithms and Directed Adaption. 103-114 - Colin R. Reeves:
Genetic Algorithms and Neighbourhood Search. 115-130 - A. Kapsalis, G. D. Smith, Victor J. Rayward-Smith:
A Unified Paradigm for Parallel Genetic Algorithms. 131-149 - Phil Husbands:
Distributed Coevolutionary Genetic Algorithms for Multi-Criteria and Multi-Constraint Optimisation. 150-165
Classifier Systems
- Andrew Fairley, Derek F. Yates:
Inductive Operators and Rule Repair in a Hybrid Genetic Learning System: Some Initial Results. 166-179 - Mukesh J. Patel, Marco Dorigo:
Adaptive Learning of a Robot Arm. 180-194 - Terence C. Fogarty:
Co-Evolving Co-Operative Populations of Rules in Learning Control Systems. 195-209 - Brian Carse:
Learning Anticipatory Behaviour Using a Delayed Action Classifier System. 210-223 - Chris Melhuish, Terence C. Fogarty:
Applying a Restricted Mating Policy to Determine State Space Niches Using Immediate and Delayed Reinforcement. 224-237 - Anthony G. Pipe, Brian Carse:
A Comparison Between Two Architectures for Searching and Learning in Maze Problems. 238-249
Applications
- David Corne, Peter Ross, Hsiao-Lan Fang:
Fast Practical Evolutionary Timetabling. 250-263 - Ben Paechter:
Optimising a Presentation Timetable Using Evolutionary Algorithms. 264-276 - Hugh M. Cartwright
, Andrew Tuson:
Genetic Algorithms and Flowshop Scheduling: Towards the Development of a Real-Time Process Control System. 277-290 - Michael S. White, Stuart J. Flockton:
Genetic Algorithms for Digital Signal Processing. 291-303 - David Beasley, David R. Bull, Ralph R. Martin:
Complexity Reduction Using Expansive Coding. 304-319 - E. Howard, N. Oakley:
The Application of Genetic Programming to the Investigation of Short, Noisy, Chaotic Data Series. 320-332
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