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An algorithmic description of XCS
Abstract A concise description of the XCS classifier system's parameters, structures, and algorithms is presented as an aid to research. The algorithms are written in modularly structured pseudo codeExpand
Toward a theory of generalization and learning in XCS
This work starts from Wilson's generalization hypothesis, which states that XCS has an intrinsic tendency to evolve accurate, maximally general classifiers, and derives a simple equation that supports the hypothesis theoretically. Expand
Rule-Based Evolutionary Online Learning Systems - A Principled Approach to LCS Analysis and Design
This paper presents a meta-analyses of the XCS Classifier System and its applications in Binary Classification Problems, Reinforcement Learning Problems, and Cognitive Learning Classifier Systems. Expand
An Algorithmic Description of XCS
This chapter provides an overview over the ACS system including all parameters as well as framework, structure, and environmental interaction, and a precise description of all algorithms in ACS2 is provided. Expand
Kernel-based, ellipsoidal conditions in the real-valued XCS classifier system
It is shown that the modifications of the XCS classifier conditions to hyperspheres and hyperellipsoids yield improved performance in continuous functions and shows that XCS is readily applicable in diverse problem domains. Expand
Gradient descent methods in learning classifier systems: improving XCS performance in multistep problems
The extension of XCS to gradient-based update methods results in a classifier system that is more robust and more parameter independent, solving large and difficult maze problems reliably. Expand
Analysis and Improvement of Fitness Exploitation in XCS: Bounding Models, Tournament Selection, and Bilateral Accuracy
This paper investigates how, when, and where accuracy-based fitness results in successful rule evolution in XCS, and introduces improvements to XCS to make fitness pressure more robust and overcome the fitness dilemma. Expand
Rule-based evolutionary online learning systems: learning bounds, classification, and prediction
The quantitative analysis of XCS shows that the interactive, evolutionary-based online learning mechanism works machine learning competitively yielding a low-order polynomial learning complexity. Expand
How XCS evolves accurate classifiers
This paper investigates what causes the XCS classifier system to evolve accurate classifiers and provides suggestions for overcoming the challenges as well as investigates environmental properties that can help XCS to overcome the challenges autonomously. Expand
Habitual and goal-directed factors in (everyday) object handling
Evidence is provided that the interaction between the habitual and the goal-directed system determines grasp selection for the interaction with every-day objects. Expand