Introducing Evolutionary Computing in Regression Analysis

Volume 5, Issue 1 (August 2014), pp. 23–27

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@article{jocse-5-1-3,
  author={Olcay Akman},
  title={Introducing Evolutionary Computing in Regression Analysis},
  journal={The Journal of Computational Science Education},
  year=2014,
  month=aug,
  volume=5,
  issue=1,
  pages={23--27},
  doi={https://doi.org/10.22369/issn.2153-4136/5/1/3}
}

A typical upper level undergraduate or first year graduate level regression course syllabus treats model selection with various stepwise regression methods. Here we implement evolutionary computing for subset model selection and accomplish two goals: i) introduce students to the powerful optimization method of genetic algorithms, and ii) transform a regression analysis course to a regression and modeling without requiring any additional time or software commitment.Furthermore we also employed Akaike Information Criterion (AIC) as a measure of model fitness instead of another commonly used measure of R-square. The model selection tool uses Excel which makes the procedure accessible to a very wide spectrum of interdisciplinary students with no specialized software requirement. An Excel macro, to be used as an instructional tool is freely available through the author's website.