Authors: Zeshan Murtza
Learning is a way which improves our ability to solve problems related to the environment surrounding us. Extended Classifier System (XCS) is a learning classifier system that use reinforcement learning mechanism to solve complex problems with robust performance. It is an accuracy-based system that works by observing environment, taking input from it and applying suitable actions. Every action of XCS gets a feedback in return from the environment which is used to improve its performance. It also has ability to apply genetic algorithm (GA) on existing classifiers and create new ones by taking cross-over and mutation which have better performance. XCS handles single step and multi-step problems by using different methods like Q-learning mechanism. The ultimate challenge of XCS is to design an implementation which arrange multiple components in a unique way to produce compact and comprehensive solution in a least amount of time. Real time implementation requires flexibility for modifications and uniqueness to cover all aspects. XCS has recently been modified for real input values and a memory management system is also introduced which enhance its ability in different kind of applications like data mining, control stock exchange. In this article, there will be a brief discussion about the parameter and components of XCS. Main part of this article will cover the extended versions of XCS with further improvements and focus on applications, usage in real environment and relationship with organic computing
Comments: 5 Pages.
[v1] 2019-03-13 10:36:13
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