DWELLS@G.BBN.COM (Dori Wells) (04/12/88)
BBN Science Development Program
AI Seminar Series
ADAPTIVE KNOWLEDGE REPRESENTATION: A CONTENT SENSITIVE
RECOMBINATION MECHANISM FOR GENETIC ALGORITHMS
J. David Schaffer
Philips Laboratories
North American Philips Corporation
Briarcliff Manor, New York
BBN Laboratories Inc.
10 Moulton Street
Large Conference Room, 2nd Floor
10:30 a.m., Tuesday, April 19, 1988
Abstract: This paper describes ongoing research on content sensitive
recombination operators for genetic algorithms. A motivation behind this
line of inquiry stems from the observation that biological chromosomes appear
to contain special nucleotide sequences whose job is to influence the
recombination of the expressible genes. We think of these as punctuation marks
telling the recombination operators how to do their job. Furthermore, we
assume that the distribution of these marks (part of the representation) in
a gene pool is determined by the same survival-of-the-fittest and genetic
recombination mechanisms that account for the distribution of the expressible
genes (the knowledge). A goal of this project is to devise such mechanisms
for genetic algorithms and thereby to link the adaptation of a representation
to the adaptation of its contents. We hope to do so in a way that capitalizes
on the intrinsically parallel behavior of the traditional genetic algorithm.
We anticipate benefits of this for machine learning.
We describe one mechanism we have devised and present some empirical evidence
that suggests it may be as good as or better than a traditional genetic
algorithm across a range of search problems. We attempt to show that its
action does successfully adapt the search mechanics to the problem space
and provide the beginnings of a theory to explain its good performance.
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