In this paper we describe the application of a learning classifier system (LCS) variant known
as the eXtended classifier system (XCS) to evolve a set of 'control rules' for a number of …

In the family of learning classifier systems, the classifier system XCS has been successfully
used for many applications. However, the standard XCS has no memory mechanism and …

This study presents an application that offers an interactive representation of the learning
cycle of a learning classifier system (LCS), a rule-based machine learning technique. The …

A main research direction in the field of evolutionary machine learning is to develop a
scalable classifier system to solve high-dimensional problems. Recently work has begun on …

This paper presents an abstract, tunable model by which to explore aspects of artificial
genetic regulatory networks and their design by simulated evolution. The random Boolean …

This paper presents a Learning Classifier System (LCS) where each traditional rule is
represented by a spiking neural network, a type of network with dynamic internal state. The …

A number of representation schemes have been presented for use within learning classifier
systems, ranging from binary encodings to neural networks. This paper presents results from …

To solve reinforcement learning problems, many learning classifier systems (LCSs) are
designed to learn state-action value functions through a compact set of maximally general …

A number of representation schemes have been presented for use within learning classifier
systems, ranging from binary encodings to artificial neural networks. This paper presents …

Knowledge representation is a key component to the success of all rule based systems
including learning classifier systems (LCSs). This component brings insight into how to …