A critical review of classifier systems
Proceedings of the third international conference on Genetic algorithms
Transition network grammars for natural language analysis
Communications of the ACM
Classifier Systems and the Animat Problem
Machine Learning
A Comparison Between ATNoSFERES And XCSM
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
Evolutionary Computation
Toward Optimal Classifier System Performance in Non-Markov Environments
Evolutionary Computation
An experimental comparison between ATNoSFERES and ACS
IWLCS'03-05 Proceedings of the 2003-2005 international conference on Learning classifier systems
Learning classifier systems: a complete introduction, review, and roadmap
Journal of Artificial Evolution and Applications
Adapted Pittsburgh classifier system: building accurate strategies in non markovian environments
Proceedings of the 10th annual conference companion on Genetic and evolutionary computation
Learning classifier systems: a complete introduction, review, and roadmap
Journal of Artificial Evolution and Applications
Dynamical genetic programming in xcsf
Evolutionary Computation
| Hi-index | 0.00 |
ATNoSFERES is a Pittsburgh style Learning Classifier System (LCS) in which the rules are represented as edges of an Augmented Transition Network. Genotypes are strings of tokens of a stack-based language, whose execution builds the labeled graph. The original ATNoSFERES, using a bitstring to represent the language tokens, has been favorably compared in previous work to several Michigan style LCSs architectures in the context of Non Markov problems. Several modifications of ATNoSFERES are proposed here: the most important one conceptually being a representational change: each token is now represented by an integer, hence the genotype is a string of integers; several other modifications of the underlying grammar language are also proposed. The resulting ATNoSFERES-II is validated on several standard animat Non Markov problems, on which it outperforms all previously published results in the LCS literature. The reasons for these improvement are carefully analyzed, and some assumptions are proposed on the underlying mechanisms in order to explain these good results.