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Context-free grammar induction with grammar-based classifier system

100%

Unold O.

2005

tom Vol. 15, no. 4

681-690

In the paper we deal with the induction of context-free grammars from sample sentences. We present some extensions to grammar-based classifier system (GCS) that evolves population of classifiers represented by context-free grammar productions rules in Chomsky Normal Form. GCS is a new version of Learning Classifier Systems but it differs from it in the covering, in the matching, and in representation. We modify the discovering component of the GCS and apply system for inferring such context-free languages as toy language, and grammar for large corpora of part-of-speech tagged natural English language. For each task a set of experiments was performed.

O krzyżowaniu wieloosobniczym w algorytmach ewolucyjnych

63%

Pliszka Z. , Unold O.

tom Vol. 55, nr 9

140-141

Artykuł podejmuje zagadnienie stosowania krzyżowania wieloosobniczego w algorytmach ewolucyjnych. Z jednej strony uściśla pewne stwierdzenia sformułowane w pracy dotyczącej krzyżowania wieloosobniczego i opublikowanej w numerze 5/2014 Elektroniki, z drugiej natomiast formułuje możliwości eksploracyjne przestrzeni rozwiązań przez tego rodzaju krzyżowanie.

The article deals with the question of the application of multi-individual crossover operator in evolutionary algorithms. On the one hand, the article specifies some of the statements on multi-individual crossovers made in the paper published in issue 5/2014 of Electronics, on the other hand formulates the possibility of solution space exploration by this type of crossing- over.

Self-adaptation of parameters in a learning classifier system ensemble machine

63%

Troć M. , Unold O.

2010

tom Vol. 20, no 1

157-174

Self-adaptation is a key feature of evolutionary algorithms (EAs). Although EAs have been used successfully to solve a wide variety of problems, the performance of this technique depends heavily on the selection of the EA parameters. Moreover, the process of setting such parameters is considered a time-consuming task. Several research works have tried to deal with this problem; however, the construction of algorithms letting the parameters adapt themselves to the problem is a critical and open problem of EAs. This work proposes a novel ensemble machine learning method that is able to learn rules, solve problems in a parallel way and adapt parameters used by its components. A self-adaptive ensemble machine consists of simultaneously working extended classifier systems (XCSs). The proposed ensemble machine may be treated as a meta classifier system. A new self-adaptive XCS-based ensemble machine was compared with two other XCS-based ensembles in relation to one-step binary problems: Multiplexer, One Counts, Hidden Parity, and randomly generated Boolean functions, in a noisy version as well. Results of the experiments have shown the ability of the model to adapt the mutation rate and the tournament size. The results are analyzed in detail.