An anticipatory approach to improve XCSF

Authors:
Amin Nikanjam;Adel Rahmani
Affiliations:
Iran University of Science and Technology, Tehran, Iran;Iran University of Science and Technology, Tehran, Iran
Venue:
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Year:
2006

Citing 1
Cited 2

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Fundamenta Informaticae
Approximating Arbitrary Reinforcement Signal by Learning Classifier Systems using Micro Genetic Algorithm

Fundamenta Informaticae

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Abstract

XCSF is a novel version of learning classifier systems (LCS) which extends the typical concept of LCS by introducing computable classifier prediction. In XCSF Classifier prediction is computed as a linear combination of classifier inputs and a weight vector associated to each classifier. Learning process takes place using a weight update mechanism. Initial results show that XCSF can be used to evolve accurate approximations of some functions. In this paper, we try to add an anticipatory component to XCSF improving its performance.