XCS has been shown to solve hard problems in a machine-learning competitive way. Recent theoretical advancements show that the system can scale-up polynomially in the problem complexity and problem size given the problem is a k-DNF with certain properties. This paper addresses two major issues in XCS: (1) knowledge extraction and (2) structure identification. Knowledge extraction addresses the ...

Computable prediction represents a major shift in learning classifier system research. XCS with computable prediction, based on linear approximators, has been applied so far to function approximation problems and to single step problems involving continuous payoff functions. In this paper we take this new approach in a different direction and apply it to the learning of Boolean functions – a do...

This paper describes updated predictions, as a function of the underlying cosmological model, for a serendipitous galaxy cluster survey that we plan to conduct with the XMM-Newton X-ray Satellite. We have included the effects of the higher than anticipated internal background count rates and have expanded our predictions to include clusters detected at > 3σ. Even with the enhanced background le...

Extended Classifier Systems, or XCS, is a soft-computing approach to machine learning in rule-based systems. While XCS has been shown effective in learning accurate, compact and complete mappings of an environment’s payoff landscape, it can require significant resources to do so. This paper presents four modifications that allow XCS to achieve high performance even in highly size-constrained po...

The purpose of this empirical study is intended to investigate XCS (Extended Classifier System) based model with knowledge rules for dynamic TIPP (Time Invariant Portfolio Protection) policy. There are two XCS-based agents in the proposed model (MA-TIPP).One agent dynamically optimizes Multiple and Tolerance variables which are concerned as the important parameters of TIPP and recommend trading...

XCS is a genetics-based machine learning model that combines reinforcement learning with evolutionary algorithms to evolve a population of classifiers in the form of condition-action rules. Like many other machine learning algorithms, XCS is less effective on high-dimensional data sets. In this paper, we describe a new guided rule discovery mechanisms for XCS, inspired by feature selection tech...

Learning Classifier Systems (LCSs), such as the accuracy-based XCS, evolve distributed problem solutions represented by a population of rules. During evolution, features are specialized, propagated, and recombined to provide increasingly accurate subsolutions. Recently, it was shown that, as in conventional genetic algorithms (GAs), some problems require efficient processing of subsets of featu...

XCS is a classifier system that combines reinforcement learning and genetic algorithms to learn a set of rules describing the knowledge inherent in a dataset. Recent studies have shown that XCS is highly competitive with respect to other classifier schemes. However, these studies have been mainly based on the analysis and improvement of the classification accuracy, paying few attention to the e...

Learning Classifier System which replaces the genetic algorithm with the evolving cooperative population of discoverers is a focus of current research. This paper presents a modified version of XCS classifier system with self-adaptive discovery module. The new model was confirmed experimentally in a multiplexer environment. The results prove that XCS with the self-adaptive method for determinin...