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Market-based reinforcement learning in partially observable worlds
- Proceedings of the International Conference on Arti Neural Networks (ICANN-2001
, 2001
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GP Classifier Problem Decomposition Using First-Price and Second-Price Auctions ∗
, 2007
"... This work details an auction-based model for problem decomposition in Genetic Programming classification. The approach builds on the population-based methodology of Genetic Programming to evolve individuals that bid high for patterns that they can correctly classify. The model returns a set of indiv ..."
Abstract
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Cited by 2 (2 self)
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This work details an auction-based model for problem decomposition in Genetic Programming classification. The approach builds on the population-based methodology of Genetic Programming to evolve individuals that bid high for patterns that they can correctly classify. The model returns a set of individuals that decompose the problem by way of this bidding process and is directly applicable to multi-class domains. An investigation of two auction types emphasizes the effect of auction design on the properties of the resulting solution. The work demonstrates that auctions are an effective mechanism for problem decomposition in classification problems and that Genetic Programming is an effective means of evolving the underlying bidding behaviour. 1
