Induction: processes of inference, learning, and discovery
Induction: processes of inference, learning, and discovery
Reducing bias and inefficiency in the selection algorithm
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
Genetic algorithms with sharing for multimodal function optimization
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
Reinforcement learning architectures for animats
Proceedings of the first international conference on simulation of adaptive behavior on From animals to animats
Lookahead planning and latent learning in a classifier system
Proceedings of the first international conference on simulation of adaptive behavior on From animals to animats
Default hierarchy formation and memory exploitation in learning classifier systems
Default hierarchy formation and memory exploitation in learning classifier systems
Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Introduction To Automata Theory, Languages, And Computation
Introduction To Automata Theory, Languages, And Computation
A Tale of Two Classifier Systems
Machine Learning
An Investigation of Niche and Species Formation in Genetic Function Optimization
Proceedings of the 3rd International Conference on Genetic Algorithms
A Study of Rule Set Development in a Learning Classifier System
Proceedings of the 3rd International Conference on Genetic Algorithms
Triggered Rule Discovery in Classifier Systems
Proceedings of the 3rd International Conference on Genetic Algorithms
A Critical Review of Classifier Systems
Proceedings of the 3rd International Conference on Genetic Algorithms
Asymptotic Dynamics of Classifier Systems
Proceedings of the 3rd International Conference on Genetic Algorithms
Efficient Exploration In Reinforcement Learning
Efficient Exploration In Reinforcement Learning
Evolution of Communication in a Population of Simple Machines
Evolution of Communication in a Population of Simple Machines
Intelligent behavior as an adaptation to the task environment
Intelligent behavior as an adaptation to the task environment
An Introduction to Anticipatory Classifier Systems
Learning Classifier Systems, From Foundations to Applications
Learning Classifier Systems, From Foundations to Applications
The Fighter Aircraft LCS: A Case of Different LCS Goals and Techniques
Learning Classifier Systems, From Foundations to Applications
A Learning Classifier Systems Bibliography
Learning Classifier Systems, From Foundations to Applications
A Roadmap to the Last Decade of Learning Classifier System Research
Learning Classifier Systems, From Foundations to Applications
A Bigger Learning Classifier Systems Bibliography
IWLCS '00 Revised Papers from the Third International Workshop on Advances in Learning Classifier Systems
A comparison between ATNoSFERES and Learning Classifier Systems on non-Markov problems
Information Sciences: an International Journal
An experimental comparison between ATNoSFERES and ACS
IWLCS'03-05 Proceedings of the 2003-2005 international conference on Learning classifier systems
Towards a mapping of modern AIS and LCS
ICARIS'11 Proceedings of the 10th international conference on Artificial immune systems
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Learning classifier systms (LCSs) offer a unique opportunity to study the adaptive exploitation of memory. Because memory is manipulated in the form of simple internal messages in the LCS, one can easily and carefully examine the development of a system of internal memory symbols. This study examines the LCS applied to a problem whose only performance goal is the effective exploitation of memory. Experimental results show that the genetic algorithm forms a relatively effective set of internal memory symbols, but that this effectiveness is directly limited by the emergence of parasite rules. The results indicate that the emergence of parasites may be an inevitable consequence in a system that must evolve its own set of internal memory symbols. The paper's primary conclusion is that the emergence of parasites is a fundamental obstacle in such problems. To overcome this obstacle, it is suggested that the LCS must form larger, multirule structures. In such structures, parasites can be more accurately evaluated and thus eliminated. This effect is demonstrated through a preliminary evaluation of a classifier corporation scheme. Final comments present future directions for research on memory exploitation in the LCS and similar evolutionary computing systems.