Imitating Success: A Memetic Crossover Operator for
Genetic Programming
B. Eskridge, and D. Hougen. Proceedings of the 2004 IEEE Congress on Evolutionary
Computation, page 809--815. Portland, Oregon, IEEE Press, (20-23 June 2004)
Abstract
For some problem domains, the evaluation of
individuals is significantly more expensive than the
other steps in the evolutionary process. Minimizing
these evaluations is vital if we want to make genetic
programming a viable strategy. In order to minimize the
required evaluations, we need to maximize the amount
learned from each evaluation. To accomplish this we
introduce a new crossover operator for genetic
programming, memetic crossover, that allows individuals
to imitate the observed success of others. An
individual that has done poorly in some parts of the
problem may then imitate an individual that did well on
those same parts. This results in an intelligent search
of the feature-space and, therefore, fewer
evaluations.
%0 Conference Paper
%1 eskridge:2004:isamcofgp
%A Eskridge, Brent
%A Hougen, Dean
%B Proceedings of the 2004 IEEE Congress on Evolutionary
Computation
%C Portland, Oregon
%D 2004
%I IEEE Press
%K Poster Session Theory algorithms, evolutionary genetic of programming,
%P 809--815
%T Imitating Success: A Memetic Crossover Operator for
Genetic Programming
%X For some problem domains, the evaluation of
individuals is significantly more expensive than the
other steps in the evolutionary process. Minimizing
these evaluations is vital if we want to make genetic
programming a viable strategy. In order to minimize the
required evaluations, we need to maximize the amount
learned from each evaluation. To accomplish this we
introduce a new crossover operator for genetic
programming, memetic crossover, that allows individuals
to imitate the observed success of others. An
individual that has done poorly in some parts of the
problem may then imitate an individual that did well on
those same parts. This results in an intelligent search
of the feature-space and, therefore, fewer
evaluations.
%@ 0-7803-8515-2
@inproceedings{eskridge:2004:isamcofgp,
abstract = {For some problem domains, the evaluation of
individuals is significantly more expensive than the
other steps in the evolutionary process. Minimizing
these evaluations is vital if we want to make genetic
programming a viable strategy. In order to minimize the
required evaluations, we need to maximize the amount
learned from each evaluation. To accomplish this we
introduce a new crossover operator for genetic
programming, memetic crossover, that allows individuals
to imitate the observed success of others. An
individual that has done poorly in some parts of the
problem may then imitate an individual that did well on
those same parts. This results in an intelligent search
of the feature-space and, therefore, fewer
evaluations.},
added-at = {2008-06-19T17:35:00.000+0200},
address = {Portland, Oregon},
author = {Eskridge, Brent and Hougen, Dean},
biburl = {https://www.bibsonomy.org/bibtex/2a9d350e006c9ff3a5b7ddbf412b1fe50/brazovayeye},
booktitle = {Proceedings of the 2004 IEEE Congress on Evolutionary
Computation},
interhash = {c85c3b792f02425ab662ebd4351c1194},
intrahash = {a9d350e006c9ff3a5b7ddbf412b1fe50},
isbn = {0-7803-8515-2},
keywords = {Poster Session Theory algorithms, evolutionary genetic of programming,},
month = {20-23 June},
notes = {CEC 2004 - A joint meeting of the IEEE, the EPS, and
the IEE.},
pages = {809--815},
publisher = {IEEE Press},
timestamp = {2008-06-19T17:39:16.000+0200},
title = {Imitating Success: {A} Memetic Crossover Operator for
Genetic Programming},
year = 2004
}