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» Gradient-Based Learning Updates Improve XCS Performance in M...
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GECCO
2004
Springer
122views Optimization» more  GECCO 2004»
13 years 10 months ago
Gradient-Based Learning Updates Improve XCS Performance in Multistep Problems
This paper introduces a gradient-based reward prediction update mechanism to the XCS classifier system as applied in neuralnetwork type learning and function approximation mechani...
Martin V. Butz, David E. Goldberg, Pier Luca Lanzi
GECCO
2008
Springer
172views Optimization» more  GECCO 2008»
13 years 6 months ago
Recursive least squares and quadratic prediction in continuous multistep problems
XCS with computed prediction, namely XCSF, has been recently extended in several ways. In particular, a novel prediction update algorithm based on recursive least squares and the ...
Daniele Loiacono, Pier Luca Lanzi
GECCO
2008
Springer
128views Optimization» more  GECCO 2008»
13 years 6 months ago
Adapted Pittsburgh classifier system: building accurate strategies in non markovian environments
This paper focuses on the study of the behavior of a genetic algorithm based classifier system, the Adapted Pittsburgh Classifier System (A.P.C.S), on maze type environments con...
Gilles Énée, Mathias Péroumal...