A Survey of Genetics-based Machine Learning Author: Tim Kovacs
Abstract:
This is a survey of the field of Genetics-based Machine Learning (GBML): the application of evolutionary algorithms to machine learning. We assume readers are familiar with evolutionary algorithms and their application to optimisation problems, but not necessarily with machine learning. We briefly outline the scope of machine learning, introduce the more specific area of supervised learning, contrast it with optimisation and present arguments for and against GBML. Next we introduce a framework for GBML which includes ways of classifying GBML algorithms and a discussion of the interaction between learning and evolution. We then review the following areas with emphasis on their evolutionary aspects: GBML for sub-problems of learning, genetic programming, evolving ensembles, evolving neural networks, learning classifier systems, and genetic fuzzy systems.
The author’s preprint has 322 references. Plus there are slides, bibliographies in BibTeX.
If you are interesting in augmented topic map authoring using GBML, this would be a good starting place.
Questions:
- Pick 3 subject areas. What arguments would you make in favor of GBML for augmenting authoring of a topic map for those subject areas?
- Same subject areas, but what arguments would you make against the use of GBML for augmenting authoring of a topic map for those subject areas?
- Design an experiment to test one of your arguments for and against GBML. (project, use of the literature encouraged)
- Convert the BibTeX formatted bibliographies into a topic map. (project)