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Darwinian Evolution in Parallel Universes: A Parallel Genetic Algorithm for Variable Selection

Summary: [This abstract is based on the authors' abstract.]The genetic algorithm (GA) seems to be a natural tool for identifying important variables that affect a particular outcome of interest arising in various industrial engineering applications. It is shown here that the GA is not a particularly effective variable selection tool, so a simple modification is proposed. A number of GAs are run in parallel without allowing each GA to fully converge, and information from all are consolidated at the end. The resulting algorithm is called the parallel genetic algorithm (PGA). Simulated and real examples show the PGA to be a competitive and easy-to-use variable selection tool.

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  • Topics:
  • Keywords: Akaike information criterion,Bayesian methods,Central Limit Theorem,Optimization,Stepwise procedures,Stochastic models,Stopping criterion,Variable selection
  • Author: Zhu, Mu; Chipman, Hugh A.
  • Journal: Technometrics