%0 Book Section %1 Kovacs:1997 %A Kovacs, Tim %B Soft computing in engineering design and manufacturing %C Berlin Heidelberg %D 1997 %E Roy, %E Chawdhry, %E Pant, %I Springer-Verlag %K LCS, XCS %P 59--68 %T XCS classifier system reliably evolves accurate, complete, and minimal representations for boolean functions. %X Wilson's recent XCS classifier system forms complete mappings of the payoff environment in the reinforcement learning tradition thanks to its accuracy based fitness. According to Wilson's Generalization Hypothesis, XCS has a tendency towards generalization. With the XCS Optimality Hypothesis, I suggest that XCS systems can evolve optimal populations (representations); populations which accurately map all input/action pairs to payoff predictions using the smallest possible set of non-overlapping classifiers. The ability of XCS to evolve optimal populations for boolean multiplexer problems is demonstrated using condensation, a technique in which evolutionary search is suspended by setting the crossover and mutation rates to zero. Condensation is automatically triggered by self-monitoring of performance statistics, and the entire learning process is terminated by autotermination. Combined, these techniques allow a classifier system to evolve optimal representations of boolean functions without any form of supervision. A more complex but more robust and efficient technique for obtaining optimal populations called subset extraction is also presented and compared to condensation. %@ ISBN 3-540-76214-0

@incollection{Kovacs:1997, abstract = {Wilson's recent XCS classifier system forms complete mappings of the payoff environment in the reinforcement learning tradition thanks to its accuracy based fitness. According to Wilson's Generalization Hypothesis, XCS has a tendency towards generalization. With the XCS Optimality Hypothesis, I suggest that XCS systems can evolve optimal populations (representations); populations which accurately map all input/action pairs to payoff predictions using the smallest possible set of non-overlapping classifiers. The ability of XCS to evolve optimal populations for boolean multiplexer problems is demonstrated using condensation, a technique in which evolutionary search is suspended by setting the crossover and mutation rates to zero. Condensation is automatically triggered by self-monitoring of performance statistics, and the entire learning process is terminated by autotermination. Combined, these techniques allow a classifier system to evolve optimal representations of boolean functions without any form of supervision. A more complex but more robust and efficient technique for obtaining optimal populations called subset extraction is also presented and compared to condensation.}, added-at = {2009-06-26T15:25:19.000+0200}, address = {Berlin Heidelberg}, author = {Kovacs, Tim}, biburl = {https://www.bibsonomy.org/bibtex/24745af65ec1668a3df7567d90113ef6e/butz}, booktitle = {{Soft computing in engineering design and manufacturing}}, description = {diverse cognitive systems bib}, editor = {Roy and Chawdhry and Pant}, interhash = {0039dab937deb26f24ff2cd2fd9f50c9}, intrahash = {4745af65ec1668a3df7567d90113ef6e}, isbn = {ISBN 3-540-76214-0}, keywords = {LCS, XCS}, pages = {59--68}, publisher = {Springer-{V}erlag}, timestamp = {2009-06-26T15:25:42.000+0200}, title = {{XCS} classifier system reliably evolves accurate, complete, and minimal representations for boolean functions.}, year = 1997 }