RandGA: injecting randomness into parallel genetic algorithm for variable selection

Chun-Xia Zhang, Guan-Wei Wang and Jun-Min Liu

Journal of Applied Statistics, 2015, vol. 42, issue 3, 630-647

Abstract: Recently, the ensemble learning approaches have been proven to be quite effective for variable selection in linear regression models. In general, a good variable selection ensemble should consist of a diverse collection of strong members. Based on the parallel genetic algorithm (PGA) proposed in [41], in this paper, we propose a novel method RandGA through injecting randomness into PGA with the aim to increase the diversity among ensemble members. Using a number of simulated data sets, we show that the newly proposed method RandGA compares favorably with other variable selection techniques. As a real example, the new method is applied to the diabetes data.

Date: 2015
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DOI: 10.1080/02664763.2014.980788

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