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 code with accompanying explanations.

Classifier systems have traditionally taken binary strings as inputs, yet in many real problems such as data inference, the inputs have real components. A modified XCS classifier system is described that learns a non-linear real-vector classification task.

This paper studies two changes to XCS, a classifier system in which fitness is based on prediction accuracy and the genetic algorithm takes place in environmental niches. The changes were aimed at increasing XCS's tendency to evolve accurate, maximally general classifiers and were tested on previously employed "woods" and multiplexer tasks. Together the changes bring XCS close to evolving populations whose high-fitness classifiers form a near-minimal, accurate, maximally general cover of the input and action product space. In addition, results on the multiplexer, a difficult categorization task, suggest that XCS's learning complexity is polynomial in the input length and thus may avoid the "curse of dimensionality", a notorious barrier to scale-up. A comparison between XCS and genetic programming in solving the 6multiplexer suggests that XCS's learning rate is about three orders of magnitude faster in terms of the number of input instances processed.

The classifier system XCS was investigated for data mining applications where the dataset discrimination surface (DS) is generally oblique to the attribute axes. Despite the classifiers' hyper-rectangular predicates, XCS reached 100% performance on synthetic problems with diagonal DS's and, in a train/test experiment, competitive performance on the Wisconsin Breast Cancer dataset. Final classifiers in an extended WBC learning run were interpretable to suggest dependencies on one or a few attributes. For data mining of numeric datasets with partially oblique discrimination surfaces, XCS shows promise from both performance and pattern discovery viewpoints.

Rule-based evolutionary online learning systems, often referred to as Michigan-style learning classifier systems (LCSs), were proposed nearly thirty years ago (Holland, 1976; Holland, 1977) originally calling them cognitive systems. LCSs combine the strength of reinforcement learning with the generalization capabilities of genetic algorithms promising a flexible, online generalizing, solely reinforcement dependent learning system. However, despite several initial successful applications of LCSs and their interesting relations with animal learning and cognition, understanding of the systems remained somewhat obscured. Questions concerning learning complexity or convergence remained unanswered. Performance in different problem types, problem structures, concept spaces, and hypothesis spaces stayed nearly unpredictable. This thesis has the following three major objectives: (1) to establish a facetwise theory approach for LCSs that promotes system analysis, understanding, and design; (2) to analyze, evaluate, and enhance the XCS classifier system (Wilson, 1995) by the means of the facetwise approach establishing a fundamental XCS learning theory; (3) to identify both the major advantages of an LCS-based learning approach as well as the most promising potential application areas. Achieving these three objectives leads to a rigorous understanding

Recently, Learning Classifier Systems (LCS) and particularly XCS have arisen as promising methods for classification tasks and data mining. This paper investigates two models of accuracy-based learning classifier systems on different types of classification problems. Departing from XCS, we analyze the evolution of a complete action map as a knowledge representation. We propose an alternative, UCS, which evolves a best action map more efficiently. We also investigate how the fitness pressure guides the search towards accurate classifiers. While XCS bases fitness on a reinforcement learning scheme, UCS defines fitness from a supervised learning scheme. We find significant differences in how the fitness pressure leads towards accuracy, and suggest the use of a supervised approach specially for multi-class problems and problems with unbalanced classes. We also investigate the complexity factors which arise in each type of accuracy-based LCS. We provide a model on the learning complexity of LCS which is based on the representative examples given to the system. The results and observations are also extended to a set of real world classification problems, where accuracy-based LCS are shown to perform competitively with respect to other learning algorithms. The work presents an extended analysis of accuracy-based LCS, gives insight into the understanding of the LCS dynamics, and suggests open issues for further improvement of LCS on classification tasks.

Due to the accuracy based fitness approach, the ultimate goal for XCS is the evolution of a compact, complete, and accurate payoff mapping of an environment. This paper investigates what causes the XCS classifier system to evolve accurate classifiers. The investigation leads to two challenges for XCS, the covering challenge and the schema challenge. Both

Despite two decades of work learning classifier systems researchers have had relatively little to say on the subject of what makes a problem difficult for a classifier system. Wilson's accuracy-based XCS, a promising and increasingly popular classifier system, is, we feel, the natural first choice of classifier system with which to address this issue.

XCS is a new kind of learning classifier system that differs from the traditional one primarily in its definition of classifier fitness and its relation to contemporary reinforcement learning. Advantages of XCS include improved performance and an ability to form accurate maximal generalizations. This paper reviews recent research on XCS with respect to representation, predictive modeling, internal state, noise, and underlying theory and technique. A notation for environmental regularities is introduced. 2 1 Introduction A classifier system is a learning system that seeks to gain reinforcement from its environment based on an evolving set of condition-action rules called classifiers. Via a Darwinian process, classifiers useful in gaining reinforcement are selected and propagate over those less useful, leading to increasing system performance. The classifier system idea is due to Holland (1986), who laid out a framework that included generalizability of classifier conditions, internal ...

After an increasing interest in learning classifier systems and the XCS classifier system in particular, this paper locates and analyzes the distinct evolutionary pressures in XCS. Combining