LCS (Learning Classifier Systems)

Learning Classifier Systems (LCS) are adaptive systems that combine machine learning with rule-based decision-making. They learn to solve problems by evolving a population of rules—called classifiers—that match different situations. When a new situation occurs, the system selects and applies the best rules to determine an action or response. Over time, through feedback and evolution, these classifiers improve their accuracy and usefulness, allowing the system to adapt to changing environments. Essentially, LCS mimic natural learning by continuously refining a set of rules to make better decisions in dynamic settings.