Comparison of Optimization Techniques based on Hebbian Learning & Genetic Algorithms

[Shumeet Baluja]

Title:
An Empirical Comparison of Seven Iterative and 
Evolutionary Function Optimization Heuristics

By:
Shumeet Baluja


Abstract:
This report is a repository for the results obtained from a large scale
empirical comparison of seven iterative and evolution-based optimization
heuristics. Twenty-seven static optimization problems, spanning six sets
of problem classes which are commonly explored in genetic algorithm
literature, are examined.  The problem sets include job-shop scheduling,
traveling salesman, knapsack, binpacking, neural network weight
optimization, and standard numerical optimization. The search spaces in
these problems range from 2^368 to 2^2040. The results indicate that using
genetic algorithms for the optimization of static functions does not yield
a benefit, in terms of the final answer obtained, over simpler
optimization heuristics.  Descriptions of the algorithms tested and the
encodings of the problems are described in detail for reproducibility.


This work may be of interest to the Artificial Neural Network community as
two of the algorithms compared are based upon simple Hebbian Learning and
supervised competitive learning algorithms.




instructions (CMU-CS-95-193)
---------------------------------------
anonymous ftp:
  ftp reports.adm.cs.cmu.edu
  binary
  cd 1995
  get CMU-CS-95-193.ps


from www:
  from my home page:  http://www.cs.cmu.edu/~baluja
  more directly:  http://www.cs.cmu.edu/afs/cs/user/baluja/www/techreps.html


if you do not have www or ftp access, send me email, 
and I will send a copy (.ps) through email.