BioHEL: Bioinformatics-oriented Hierarchical Evolutionary Learning

Bacardit, Jaume; Krasnogor, Natalio

Abstract

This technical report briefly describes our recent work in the iterative
rule learning approach (IRL) of evolutionary learning/genetics-based machine learning. This approach was initiated by the SIA system.
A more recent example is HIDER. Our approach integrates some of the main characteristics of GAssist, a system belonging to the Pittsburgh approach of Evolutionary Learning, into the general framework of IRL. Our aims in developing this system are use all the good characteristics of GAssist but at the same time overcome some of the scalability limitations that it presents.

Citation

Bacardit, J., & Krasnogor, N. (2006). BioHEL: Bioinformatics-oriented Hierarchical Evolutionary Learning. Computer Science & IT

• Book Type: Monograph
• Publication Date: Jan 1, 2006
• Deposit Date: Apr 17, 2007
• Publicly Available Date: Oct 9, 2007
• Peer Reviewed: Not Peer Reviewed
• Keywords: Machine Learning, Data Mining, Learning Classifier Systems, Evolutionary Computation
• Public URL: https://nottingham-repository.worktribe.com/output/1018665