XCS, a Genetic Based Machine Learning model that combines reinforcement learning with evolutionary algorithms to evolve a population of classifiers in the form of condition-action rules, has been used successfully for many classification tasks. However, like many other machine learning algorithms, XCS becomes less effective when it is applied to high-dimensional data sets. In this paper, we pre...

Due to the accuracy based tness approach, the ultimate goal for XCS is the evolution of a compact, complete, and accurate payo mapping of an environment. This paper investigates what causes the XCS classi er system to evolve accurate classi ers. The investigation leads to two challenges for XCS, the covering challenge and the schema challenge. Both challenges are revealed theoretically and expe...

The accuracy-based classifier system XCS is currently the most successful learning classifier system. Several recent studies showed that XCS can produce machine-learning competitive results. Nonetheless, until now the evolutionary mechanisms in XCS remained somewhat ill-understood. This study investigates the selectorecombinative capabilities of the current XCS system. We reveal the accuracy de...

The XCS classiier system was developed by Wilson (1995). The learning mechanism is based on the accuracy of its reward prediction. This method leads to the formation of accurate most general classiiers. This paper explains how to download, compile and use the XCS code version 1.0 written in ANSI C. It discusses how to select various parameter settings, how to add and remove certain procedures i...

Despite several recent successful comparisons and applications of the accuracy-based learning classifier system XCS, it is hardly understood how crucial parameters should be set in XCS nor how XCS can be expect to scale up in larger problems. Previous research identified a covering challenge in XCS that needs to be obeyed to ensure that the genetic learning process takes place. Furthermore, a s...

Evolutionary Learning Classifier Systems (ELCS) were introduced by Holland a few decades ago. Since their birth, they were successfully applied to various data analysis domains. XCS is currently considered as state of the art ELCS. Earlier work have experimented with XCS on artificial problems or small datasets, and shown good results. However, XCS has not been tested on large datasets, particu...

The regulation of the cell cycle during early development is an important and complex biological process. We have cloned a cDNA, XCS-1, that may play an important role in regulating mitosis during early embryogenesis in Xenopus laevis. XCS-1 is a maternally expressed gene product that is the Xenopus homologue of the human cleavage signal protein (CS-1). XCS-1 transcripts were detected in oocyte...

This paper introduces a gradient-based reward prediction update mechanism to the XCS classifier system as applied in neuralnetwork type learning and function approximation mechanisms. A strong relation of XCS to tabular reinforcement learning and more importantly to neural-based reinforcement learning techniques is drawn. The resulting gradient-based XCS system learns more stable and reliable i...

The performance of a learning classifier system is due to its two main components. First, it evolves new structures by generating new rules in a genetic process; second, it adjusts parameters of existing rules, for example rule prediction and accuracy, in an evaluation step, which is not only important for applying the rules, but also for the genetic process. The two components interleave and i...