Message-based bucket brigade: an algorithm for the apportionment of credit problem
EWSL-91 Proceedings of the European working session on learning on Machine learning
Adaptation in natural and artificial systems
Adaptation in natural and artificial 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
Hidden order: how adaptation builds complexity
Hidden order: how adaptation builds complexity
Emergence: from chaos to order
Emergence: from chaos to order
The Design of Intelligent Agents: A Layered Approach
The Design of Intelligent Agents: A Layered Approach
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
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An agent population can be evolved in a complex environment to perform various tasks and optimize its job performance using Learning Classifier System (LCS) technology. Due to the complexity and knowledge content of some real-world systems, having the ability to use genetic programming, GP, to represent the LCS rules provides a great benefit. Methods have been created to extend LCS theory into operation across the power-set of GP-enabled rule content. This system uses a full bucket-brigade system for GP-LCS learning. Using GP in the LCS system allows the functions and terminals of the actual problem environment to be used internally directly in the rule set, enabling more direct interpretation of the operation of the LCS system. The system was designed and built, and underwent a year of independent testing at an advanced technology research laboratory. This paper describes the top-level operation of the system, and includes some of the results of the testing effort, and performance figures.