- author = "Ari Juels and Martin Wattenberg",
- title = "Stochastic Hillclimbing as a Baseline Method for Evaluating Genetic Algorithms",
- institution = "Department of Computer Science, University of California at Berkeley",
- year = "1995",
- type = "Technical Report",
- number = "CSD-94-834",
- address = "USA",
- month = "18 " # jul,
- keywords = "genetic algorithms, genetic programming",
-
URL = "
http://www.eecs.berkeley.edu/Pubs/TechRpts/1994/CSD-94-834.pdf",
-
URL = "
http://citeseer.nj.nec.com/juels94stochastic.html",
- abstract = "We investigate the effectiveness of stochastic hillclimbing as a baseline for evaluating the performance of genetic algorithms (GAs) as combinatorial function optimisers. In particular, we address four problems to which GAs have been applied in the literature: the maximum-cut problem, Koza's 11-multiplexer problem, MDAP (the Multiprocessor Document Allocation Problem), and the jobshop problem. We demonstrate that simple stochastic hill climbing methods are able to achieve results comparable or superior to those obtained by the GAs designed to address these four problems. We further illustrate, in the case of the jobshop problem, how insights obtained in the formulation of a stochastic hill-climbing algorithm can lead to improvements in the encoding used by a GA.",
-
notes = "{"}We demonstate that simple stochastic hillcliming
methods are able to achieve results comparable or
superior to those obtained by the GAs{"}. 4 GAs one is
Koza's
11-multiplexor.
citeseer.nj.nec.com/juels94stochastic.html may be slightly different from CSD-94-834",
