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
A Survey of Optimization by Building and Using Probabilistic Models
Computational Optimization and Applications
From Recombination of Genes to the Estimation of Distributions I. Binary Parameters
PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
Get Real! XCS with Continuous-Valued Inputs
Learning Classifier Systems, From Foundations to Applications
An Algorithmic Description of XCS
IWLCS '00 Revised Papers from the Third International Workshop on Advances in Learning Classifier Systems
IWLCS '00 Revised Papers from the Third International Workshop on Advances in Learning Classifier Systems
Automated global structure extraction for effective local building block processing in XCS
Evolutionary Computation
Investigating scaling of an abstracted LCS utilising ternary and s-expression alphabets
Proceedings of the 9th annual conference companion on Genetic and evolutionary computation
HIS '07 Proceedings of the 7th International Conference on Hybrid Intelligent Systems
Classifier fitness based on accuracy
Evolutionary Computation
Design and Analysis of Learning Classifier Systems: A Probabilistic Approach (Studies in Computational Intelligence)
Classifier Conditions Using Gene Expression Programming
Learning Classifier Systems
Learning classifier systems: a complete introduction, review, and roadmap
Journal of Artificial Evolution and Applications
Automatically defined functions for learning classifier systems
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
XCSR with computed continuous action
AI'12 Proceedings of the 25th Australasian joint conference on Advances in Artificial Intelligence
Proceedings of the 15th annual conference on Genetic and evolutionary computation
Comparison of two methods for computing action values in XCS with code-fragment actions
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
The subsumption mechanism for XCS using code fragmented conditions
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
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Human beings have the ability to apply the domain knowledge learned from a smaller problem to more complex problems of the same or a related domain, but currently evolutionary computation techniques lack this ability. Hence these techniques relearn from the start when the problem scales, increasing the time required and potentially limiting capability. In order to autonomously scale in a problem domain reusable building blocks of knowledge must be extracted. A richer encoding scheme than ternary alphabet has been constructed to identify building blocks. The novel work presented here is to extract useful building blocks from smaller problems and reuse them to learn complex problems in the domain. The proposed system has been compared with ternary alphabet based XCS for three different problem domains, i.e. multiplexer, carry, and even-parity problems. Autonomous scaling is shown possible for the first time in learning classifier systems. It improves effectiveness and reduces the number of training instances required in large problems, but requires more time due to more involved methods.