Reducing Bloat and Promoting Diversity using
Multi-Objective Methods
E. de Jong, R. Watson, and J. Pollack. Proceedings of the Genetic and Evolutionary
Computation Conference (GECCO-2001), page 11--18. San Francisco, California, USA, Morgan Kaufmann, (7-11 July 2001)
Abstract
Two important problems in genetic programming (GP) are
its tendency to find unnecessarily large trees (bloat),
and the general evolutionary algorithms problem that
diversity in the population can be lost prematurely.
The prevention of these problems is frequently an
implicit goal of basic GP. We explore the potential of
techniques from multi-objective optimization to aid GP
by adding explicit objectives to avoid bloat and
promote diversity. The even 3, 4, and 5-parity problems
were solved efficiently compared to basic GP results
from the literature. Even though only non-dominated
individuals were selected and populations thus remained
extremely small, appropriate diversity was maintained.
The size of individuals visited during search
consistently remained small, and solutions of what we
believe to be the minimum size were found for the 3, 4,
and 5-parity problems.
Proceedings of the Genetic and Evolutionary
Computation Conference (GECCO-2001)
year
2001
month
7-11 July
pages
11--18
publisher
Morgan Kaufmann
publisher_address
San Francisco, CA 94104, USA
isbn
1-55860-774-9
notes
GECCO-2001 A joint meeting of the tenth International
Conference on Genetic Algorithms (ICGA-2001) and the
sixth Annual Genetic Programming Conference (GP-2001)
Part of spector:2001:GECCO
%0 Conference Paper
%1 jong:2001:gecco
%A de Jong, Edwin D.
%A Watson, Richard A.
%A Pollack, Jordan B.
%B Proceedings of the Genetic and Evolutionary
Computation Conference (GECCO-2001)
%C San Francisco, California, USA
%D 2001
%E Spector, Lee
%E Goodman, Erik D.
%E Wu, Annie
%E Langdon, W. B.
%E Voigt, Hans-Michael
%E Gen, Mitsuo
%E Sen, Sandip
%E Dorigo, Marco
%E Pezeshk, Shahram
%E Garzon, Max H.
%E Burke, Edmund
%I Morgan Kaufmann
%K Pareto, algorithms, bloat, code diversity evolutionary genetic growth, introns, maintenance, multi-objective optimality optimization, programming,
%P 11--18
%T Reducing Bloat and Promoting Diversity using
Multi-Objective Methods
%U http://citeseer.ist.psu.edu/440305.html
%X Two important problems in genetic programming (GP) are
its tendency to find unnecessarily large trees (bloat),
and the general evolutionary algorithms problem that
diversity in the population can be lost prematurely.
The prevention of these problems is frequently an
implicit goal of basic GP. We explore the potential of
techniques from multi-objective optimization to aid GP
by adding explicit objectives to avoid bloat and
promote diversity. The even 3, 4, and 5-parity problems
were solved efficiently compared to basic GP results
from the literature. Even though only non-dominated
individuals were selected and populations thus remained
extremely small, appropriate diversity was maintained.
The size of individuals visited during search
consistently remained small, and solutions of what we
believe to be the minimum size were found for the 3, 4,
and 5-parity problems.
%@ 1-55860-774-9
@inproceedings{jong:2001:gecco,
abstract = {Two important problems in genetic programming (GP) are
its tendency to find unnecessarily large trees (bloat),
and the general evolutionary algorithms problem that
diversity in the population can be lost prematurely.
The prevention of these problems is frequently an
implicit goal of basic GP. We explore the potential of
techniques from multi-objective optimization to aid GP
by adding explicit objectives to avoid bloat and
promote diversity. The even 3, 4, and 5-parity problems
were solved efficiently compared to basic GP results
from the literature. Even though only non-dominated
individuals were selected and populations thus remained
extremely small, appropriate diversity was maintained.
The size of individuals visited during search
consistently remained small, and solutions of what we
believe to be the minimum size were found for the 3, 4,
and 5-parity problems.},
added-at = {2008-06-19T17:35:00.000+0200},
address = {San Francisco, California, USA},
author = {{de Jong}, Edwin D. and Watson, Richard A. and Pollack, Jordan B.},
biburl = {https://www.bibsonomy.org/bibtex/2e3e9434b31c92725e18f02e10e74f1ed/brazovayeye},
booktitle = {Proceedings of the Genetic and Evolutionary
Computation Conference (GECCO-2001)},
editor = {Spector, Lee and Goodman, Erik D. and Wu, Annie and Langdon, W. B. and Voigt, Hans-Michael and Gen, Mitsuo and Sen, Sandip and Dorigo, Marco and Pezeshk, Shahram and Garzon, Max H. and Burke, Edmund},
interhash = {1fe24faa4226d7d4a14c7da79a017929},
intrahash = {e3e9434b31c92725e18f02e10e74f1ed},
isbn = {1-55860-774-9},
keywords = {Pareto, algorithms, bloat, code diversity evolutionary genetic growth, introns, maintenance, multi-objective optimality optimization, programming,},
month = {7-11 July},
notes = {GECCO-2001 A joint meeting of the tenth International
Conference on Genetic Algorithms (ICGA-2001) and the
sixth Annual Genetic Programming Conference (GP-2001)
Part of \cite{spector:2001:GECCO}},
pages = {11--18},
publisher = {Morgan Kaufmann},
publisher_address = {San Francisco, CA 94104, USA},
timestamp = {2008-06-19T17:38:33.000+0200},
title = {Reducing Bloat and Promoting Diversity using
Multi-Objective Methods},
url = {http://citeseer.ist.psu.edu/440305.html},
year = 2001
}