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Learning Classifier Systems (LCSs) have gained increasing interest in the genetic and evolutionary computation literature. Many real-world problems are not conveniently expressed using the ternary representation typically used by LCSs and for such problems an interval-based representation is preferable. A new model of LCSs is introduced to classify(More)
The research into the ability of building self-learning natural language parser based on context–free grammar (CFG ) was presented. The paper investigates the use of evolutionary methods: a genetic algorithm, a genetic programming and learning classifier systems for inferring CFG based parser. The experiments were conducted on the real set of natural(More)
Amyloids are proteins capable of forming fibrils. Many of them underlie serious diseases, like Alzheimer disease. The number of amyloid-associated diseases is constantly increasing. Recent studies indicate that amyloidogenic properties can be associated with short segments of aminoacids, which transform the structure when exposed. A few hundreds of such(More)
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(More)
The grammar-based classifier system (GCS) is a new version of learning classifier systems (LCS) in which classifiers are represented by context-free grammar in Chomsky normal form. GCS evolves one grammar during induction (the Michigan approach) which gives it the ability to find the proper set of rules very quickly. However it is quite sensitive to any(More)
The paper introduces accuracy boosting extension to a novel induction of fuzzy rules from raw data using Artificial ImmuneSystemmethods.Accuracyboosting relieson fuzzypartition learning. Theperformance, in terms of classification accuracy, of the proposed approach was compared with traditional classifier ccepted 21 June 2010 vailable online 1 July 2010