paper: Removing the Genetics from the Standard Genetic Algorithm

[Shumeet Baluja]

Title:
Removing the Genetics from the Standard Genetic Algorithm


By:
Shumeet Baluja and Rich Caruana


Abstract:
We present an abstraction of the genetic algorithm (GA), termed
population-based incremental learning (PBIL), that explicitly
maintains the statistics contained in a GA's population, but
which abstracts away the crossover operator and redefines the
role of the population. This results in PBIL being simpler,
both computationally and theoretically, than the GA. Empirical
results reported elsewhere show that PBIL is faster and more
effective than the GA on a large set of commonly used benchmark
problems.  Here we present results on a problem custom designed
to benefit both from the GA's crossover operator and from its
use of a population. The results show that PBIL performs as
well as, or better than, GAs carefully tuned to do well on this
problem. This suggests that even on problems custom designed
for GAs, much of the power of the GA may derive from the
statistics maintained implicitly in its population, and not
from the population itself nor from the crossover operator.


This paper may be of interest to the connectionist community as
the PBIL algorithm is largely based upon supervised
competitive learning algorithms.


This paper will appear in the Proceedings of the International
Confernece on Machine Learning, 1995.



instructions
----------------------------------------------

via anonymous ftp at:

reports.adm.cs.cmu.edu


once you are logged in,
issue the following commands:

    binary
    cd 1995
    get CMU-CS-95-141.ps