Stochastic Hillclimbing as a Baseline Method for Evaluating Genetic Algorithms
Abstract
We investigate the effectiveness of stochastic hillclimbing as a baseline for evaluating the performance of genetic algorithms (GAs) as combinato(cid:173) rial function optimizers. In particular, we address two problems to which GAs have been applied in the literature: Koza's ll-multiplexer problem and the jobshop problem. We demonstrate that simple stochastic hill(cid:173) climbing methods are able to achieve results comparable or superior to those obtained by the GAs designed to address these two problems. We further illustrate, in the case of the jobshop problem, how insights ob(cid:173) tained in the formulation of a stochastic hillclimbing algorithm can lead to improvements in the encoding used by a GA.
Cite
Text
Juels and Wattenberg. "Stochastic Hillclimbing as a Baseline Method for Evaluating Genetic Algorithms." Neural Information Processing Systems, 1995.Markdown
[Juels and Wattenberg. "Stochastic Hillclimbing as a Baseline Method for Evaluating Genetic Algorithms." Neural Information Processing Systems, 1995.](https://mlanthology.org/neurips/1995/juels1995neurips-stochastic/)BibTeX
@inproceedings{juels1995neurips-stochastic,
title = {{Stochastic Hillclimbing as a Baseline Method for Evaluating Genetic Algorithms}},
author = {Juels, Ari and Wattenberg, Martin},
booktitle = {Neural Information Processing Systems},
year = {1995},
pages = {430-436},
url = {https://mlanthology.org/neurips/1995/juels1995neurips-stochastic/}
}