Journal of the American College Surgeons 236(5):p S136, May 2023. | DOI: 10.1097/01.XCS.0000933524.61211.70

The development of the XCS Learning Classifier System [26] has produced a stable implementation, able to consistently identify the accurate and optimally general population of classifiers mapping a given reward landscape [15,16,29]. XCS is particularly powerful within directreward environments, and notably within problems suitable for commercial application [3]. The application of XCS within de...

This work investigates some uses of self-monitoring in classi er systems (CS) using Wilson's recent XCS system as a framework. XCS is a signi cant advance in classi er systems technology which shifts the basis of tness evaluation for the Genetic Algorithm (GA) from the strength of payo prediction to the accuracy of payo prediction. Initial work consisted of implementing an XCS system in Pop11 a...

The extended classifier systems (XCS) by producing a set of rules is (classifier) trying to solve learning problems as online. XCS is a rather complex combination of genetic algorithm and reinforcement learning that using genetic algorithm tries to discover the encouraging rules and value them by reinforcement learning. Among the important factors in the performance of XCS is the possibility to...

We analyze the generalization behavior of the XCS classi er system in environments in which only a few generalizations can be done. Experimental results presented in the paper evidence that the generalization mechanism of XCS can prevent it from learning even simple tasks in such environments. We present a new operator, named Specify, which contributes to the solution of this problem. XCS with ...

Wilson's recent XCS classiier system forms complete mappings of the payoo environment in the reinforcement learning tradition thanks to its accuracy based tness. According to Wilson's Generalization Hypothesis, XCS has a tendency towards generalization. With the XCS Optimality Hypothesis, I suggest that XCS systems can evolve optimal populations (representations); populations which accurately m...

The Learning Classifier System (LCS) and its descendant, XCS, are promising paradigms for adaptive agent construction. Whereas LCS allows classifier payoff predictions to guide system performance, XCS focuses on payoff-prediction accuracy instead, allowing it to evolve “optimal” classifier sets in particular applications requiring rational thought. We examine LCS/XCS performance in artificial s...

There are several studies about extended classification system (XCS) in past years. XCS model can dynamically learn and adapt to the change of environments for maximizing the desired goals. This paper conducts simulation to apply XCS to global asset allocation in the country-specific exchanged traded funds (ETFs). Since international stock price trend is influenced by unknown and unpredictable ...

Recognizing that many payoff functions are continuous and depend on the input state x, the classifier system architecture XCS is extended so that a classifier’s prediction is a linear function of x. On a continuous nonlinear problem, the extended system, XCS-LP, exhibits high performance and low error, as well as dramatically smaller evolved populations compared with XCS. Linear predictions are...