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Evolving process-based models from psychological data using genetic programming
| Content Provider | Semantic Scholar |
|---|---|
| Author | Lane, Peter C. R. Sozou, Peter D. Addison, Mark Gobet, Fernand |
| Copyright Year | 2014 |
| Abstract | The development of computational models to provide explanations of psychological data can be achieved using semi-automated search techniques, such as genetic programming. One challenge with these techniques is to control the type of model that is evolved to be cognitively plausible – a typical problem is that of “bloating”, where continued evolution generates models of increasing size without improving overall fitness. In this paper we describe a system for representing psychological data, a class of process-based models, and algorithms for evolving models. We apply this system to the delayed match-to-sample task. We show how the challenge of bloating may be addressed by extending the fitness function to include measures of cognitive performance. |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | http://eprints.lse.ac.uk/66170/1/__lse.ac.uk_storage_LIBRARY_Secondary_libfile_shared_repository_Content_Sozou,P.D_Sozou_Evolving_Process-Based_Models.pdf |
| Alternate Webpage(s) | http://chrest.info/fg/Conf-Papers-2010-2015/Lane-2014-AISB50-EvolvingProcessBasedModels.pdf |
| Alternate Webpage(s) | http://doc.gold.ac.uk/aisb50/AISB50-S06/AISB50-S6-Lane-paper.pdf |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
