Natural selection at work: an accelerated evolutionary computing approach to predictive model selection
We implement genetic algorithm based predictive model building as an alternative to the traditional stepwise regression. We then employ the Information Complexity Measure (ICOMP) as a measure of model fitness instead of the commonly used measure of <em>R</em>-square. Furthermore, we prop...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2010-07-01
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| Series: | Frontiers in Neuroscience |
| Subjects: | |
| Online Access: | http://journal.frontiersin.org/Journal/10.3389/fnins.2010.00033/full |
| _version_ | 1818551648487735296 |
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| author | Olcay Akman Joshua Hallam |
| author_facet | Olcay Akman Joshua Hallam |
| author_sort | Olcay Akman |
| collection | DOAJ |
| description | We implement genetic algorithm based predictive model building as an alternative to the traditional stepwise regression. We then employ the Information Complexity Measure (ICOMP) as a measure of model fitness instead of the commonly used measure of <em>R</em>-square. Furthermore, we propose some modifications to the genetic algorithm to increase the overall efficiency. |
| first_indexed | 2024-12-12T09:02:42Z |
| format | Article |
| id | doaj.art-6c5b90a55cae489691212ad45791b47c |
| institution | Directory Open Access Journal |
| issn | 1662-453X |
| language | English |
| last_indexed | 2024-12-12T09:02:42Z |
| publishDate | 2010-07-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Neuroscience |
| spelling | doaj.art-6c5b90a55cae489691212ad45791b47c2022-12-22T00:29:47ZengFrontiers Media S.A.Frontiers in Neuroscience1662-453X2010-07-01410.3389/fnins.2010.000331730Natural selection at work: an accelerated evolutionary computing approach to predictive model selectionOlcay Akman0Joshua Hallam1Illinois State UniversityMichigan State UniversityWe implement genetic algorithm based predictive model building as an alternative to the traditional stepwise regression. We then employ the Information Complexity Measure (ICOMP) as a measure of model fitness instead of the commonly used measure of <em>R</em>-square. Furthermore, we propose some modifications to the genetic algorithm to increase the overall efficiency.http://journal.frontiersin.org/Journal/10.3389/fnins.2010.00033/fullgenetic algorithmsDiversificationinformation complexity measurepopulation reductionstepwise regression |
| spellingShingle | Olcay Akman Joshua Hallam Natural selection at work: an accelerated evolutionary computing approach to predictive model selection Frontiers in Neuroscience genetic algorithms Diversification information complexity measure population reduction stepwise regression |
| title | Natural selection at work: an accelerated evolutionary computing approach to predictive model selection |
| title_full | Natural selection at work: an accelerated evolutionary computing approach to predictive model selection |
| title_fullStr | Natural selection at work: an accelerated evolutionary computing approach to predictive model selection |
| title_full_unstemmed | Natural selection at work: an accelerated evolutionary computing approach to predictive model selection |
| title_short | Natural selection at work: an accelerated evolutionary computing approach to predictive model selection |
| title_sort | natural selection at work an accelerated evolutionary computing approach to predictive model selection |
| topic | genetic algorithms Diversification information complexity measure population reduction stepwise regression |
| url | http://journal.frontiersin.org/Journal/10.3389/fnins.2010.00033/full |
| work_keys_str_mv | AT olcayakman naturalselectionatworkanacceleratedevolutionarycomputingapproachtopredictivemodelselection AT joshuahallam naturalselectionatworkanacceleratedevolutionarycomputingapproachtopredictivemodelselection |