Skip to main content


Showing posts from January, 2011

Modern Learning Classifier Systems

From the classic point of view, machine learning algorithms are classified based on the desired outcome of the algorithm. There are three main types of learning: supervised learning, where an expert or teacher provides feedback in the learning process, unsupervised learning, where there is no expert or teacher when the learning process is running, and reinforcement learning, where the program learns interacting with the environment. The latter technique of learning is a fundamental mechanism in learning classifier systems (LCSs). These are cognitive systems that receive perceptions from the environment and, in response to these, perform actions to solve the problem that are facing.
Originally proposed by John Holland and later simplified by David Goldberg and others, LCSs are computer programs that are based on observations of how natural selection processes and Darwinian evolution solve complex tasks. The original purpose was to create true artificial intelligence mimicking the adap…