A Learning Classifier System with Mutual-Information-Based Fitness

Authors:
Robert Elliott Smith;Max Kun Jiang
Affiliations:
Department of Computer Science, University College London, London, United Kingdom;Department of Computer Science, University College London, London, United Kingdom
Venue:
Learning Classifier Systems
Year:
2008

Citing 10
Cited 0

Induction: processes of inference, learning, and discovery

Induction: processes of inference, learning, and discovery
The cascade-correlation learning architecture

Advances in neural information processing systems 2
Adaptation in natural and artificial systems

Adaptation in natural and artificial systems
Genetic Algorithms in Search, Optimization and Machine Learning

Genetic Algorithms in Search, Optimization and Machine Learning
Introduction to Reinforcement Learning

Introduction to Reinforcement Learning
Accuracy-based learning classifier systems: models, analysis and applications to classification tasks

Evolutionary Computation
Zcs: A zeroth level classifier system

Evolutionary Computation
Is a learning classifier system a type of neural network?

Evolutionary Computation
Classifier fitness based on accuracy

Evolutionary Computation
From HP lattice models to real proteins: coordination number prediction using learning classifier systems

EuroGP'06 Proceedings of the 2006 international conference on Applications of Evolutionary Computing

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper introduces a new variety of learning classifier system (LCS), called MILCS, which utilizes mutual information as fitness feedback. Unlike most LCSs, MILCS is specifically designed for supervised learning. We present preliminary results, and contrast them to results from XCS. We discuss the explanatory power of the resulting rule sets and introduce a new technique for visualizing explanatory power. Final comments include future directions of this research, including investigations in neural networks and other systems.