How to use crowding selection in grammar-based classifier system

Abstract

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 variations of learning parameters. This paper investigates the role of crowding selection in GCS. To evaluate the performance of GCS depending on crowding factor and crowding subpopulation we used context-free language in the form of so-called toy language. The set of experiments was performed to obtain the answer for question in the title.