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GECCO
2004
Springer
122views Optimization» more  GECCO 2004»
13 years 11 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
2006
Springer
177views Optimization» more  GECCO 2006»
13 years 9 months ago
Hyper-ellipsoidal conditions in XCS: rotation, linear approximation, and solution structure
The learning classifier system XCS is an iterative rulelearning system that evolves rule structures based on gradient-based prediction and rule quality estimates. Besides classifi...
Martin V. Butz, Pier Luca Lanzi, Stewart W. Wilson
GECCO
2005
Springer
13 years 11 months ago
XCS with computed prediction in multistep environments
XCSF extends the typical concept of learning classifier sys
Pier Luca Lanzi, Daniele Loiacono, Stewart W. Wils...
CEC
2007
IEEE
13 years 9 months ago
Support vector machines for computing action mappings in learning classifier systems
XCS with Computed Action, briefly XCSCA, is a recent extension of XCS to tackle problems involving a large number of discrete actions. In XCSCA the classifier action is computed wi...
Daniele Loiacono, Andrea Marelli, Pier Luca Lanzi
CEC
2005
IEEE
13 years 7 months ago
XCS with computed prediction in continuous multistep environments
We apply XCS with computed prediction (XCSF) to tackle multistep reinforcement learning problems involving continuous inputs. In essence we use XCSF as a method of generalized rein...
Pier Luca Lanzi, Daniele Loiacono, Stewart W. Wils...