BioHEL: Bioinformatics-oriented Hierarchical Evolutionary Learning
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 G...
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| Format: | Monograph |
| Published: |
Computer Science & IT
2006
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| Online Access: | https://eprints.nottingham.ac.uk/482/ |
| _version_ | 1848790417421631488 |
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| author | Bacardit, Jaume Krasnogor, Natalio |
| author_facet | Bacardit, Jaume Krasnogor, Natalio |
| author_sort | Bacardit, Jaume |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-14T18:12:17Z |
| format | Monograph |
| id | nottingham-482 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| last_indexed | 2025-11-14T18:12:17Z |
| publishDate | 2006 |
| publisher | Computer Science & IT |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-4822020-05-04T20:29:43Z https://eprints.nottingham.ac.uk/482/ BioHEL: Bioinformatics-oriented Hierarchical Evolutionary Learning Bacardit, Jaume Krasnogor, Natalio 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. Computer Science & IT 2006 Monograph NonPeerReviewed Bacardit, Jaume and Krasnogor, Natalio (2006) BioHEL: Bioinformatics-oriented Hierarchical Evolutionary Learning. Technical Report. Computer Science & IT, University of Nottingham. (Unpublished) Machine Learning Data Mining Learning Classifier Systems Evolutionary Computation |
| spellingShingle | Machine Learning Data Mining Learning Classifier Systems Evolutionary Computation Bacardit, Jaume Krasnogor, Natalio BioHEL: Bioinformatics-oriented Hierarchical Evolutionary Learning |
| title | BioHEL: Bioinformatics-oriented Hierarchical Evolutionary Learning |
| title_full | BioHEL: Bioinformatics-oriented Hierarchical Evolutionary Learning |
| title_fullStr | BioHEL: Bioinformatics-oriented Hierarchical Evolutionary Learning |
| title_full_unstemmed | BioHEL: Bioinformatics-oriented Hierarchical Evolutionary Learning |
| title_short | BioHEL: Bioinformatics-oriented Hierarchical Evolutionary Learning |
| title_sort | biohel: bioinformatics-oriented hierarchical evolutionary learning |
| topic | Machine Learning Data Mining Learning Classifier Systems Evolutionary Computation |
| url | https://eprints.nottingham.ac.uk/482/ |