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Bacardit, Jaume and Krasnogor, Natalio (2006) BioHEL: Bioinformatics-oriented Hierarchical Evolutionary Learning. Technical Report. Computer Science & IT, University of Nottingham. (Unpublished)

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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.

Item Type:	Monograph (Technical Report)
RIS ID:	https://nottingham-repository.worktribe.com/output/1018665
Keywords:	Machine Learning, Data Mining, Learning Classifier Systems, Evolutionary Computation
Schools/Departments:	University of Nottingham, UK > Faculty of Science > School of Computer Science
Depositing User:	Bacardit, Jaume
Date Deposited:	17 Apr 2007
Last Modified:	04 May 2020 20:29
URI:	https://eprints.nottingham.ac.uk/id/eprint/482

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