ML Anthology

Genetic Algorithms and Explicit Search Statistics

Shumeet Baluja
NeurIPS 1996 pp. 319-325
/neurips/1996/baluja1996neurips-genetic/

Abstract

The genetic algorithm (GA) is a heuristic search procedure based on mechanisms abstracted from population genetics. In a previous paper [Baluja & Caruana, 1995], we showed that much simpler algorithms, such as hillcIimbing and Population(cid:173) Based Incremental Learning (PBIL), perform comparably to GAs on an optimiza(cid:173) tion problem custom designed to benefit from the GA's operators. This paper extends these results in two directions. First, in a large-scale empirical comparison of problems that have been reported in GA literature, we show that on many prob(cid:173) lems, simpler algorithms can perform significantly better than GAs. Second, we describe when crossover is useful, and show how it can be incorporated into PBIL.

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Text

Baluja. "Genetic Algorithms and Explicit Search Statistics." Neural Information Processing Systems, 1996.

Markdown

[Baluja. "Genetic Algorithms and Explicit Search Statistics." Neural Information Processing Systems, 1996.](https://mlanthology.org/neurips/1996/baluja1996neurips-genetic/)

BibTeX

@inproceedings{baluja1996neurips-genetic,
  title     = {{Genetic Algorithms and Explicit Search Statistics}},
  author    = {Baluja, Shumeet},
  booktitle = {Neural Information Processing Systems},
  year      = {1996},
  pages     = {319-325},
  url       = {https://mlanthology.org/neurips/1996/baluja1996neurips-genetic/}
}