Empirical Comparison of Incremental Reuse Strategies in Genetic Programming for Keep-Away Soccer
Created by W.Langdon from
gp-bibliography.bib Revision:1.8081
- @InProceedings{hsu:2004:lbp,
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author = "William H. Hsu and Scott J. Harmon and
Edwin Rodriguez and Christopher Zhong",
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title = "Empirical Comparison of Incremental Reuse Strategies
in Genetic Programming for Keep-Away Soccer",
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booktitle = "Late Breaking Papers at the 2004 Genetic and
Evolutionary Computation Conference",
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year = "2004",
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editor = "Maarten Keijzer",
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address = "Seattle, Washington, USA",
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month = "26 " # jul,
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keywords = "genetic algorithms, genetic programming",
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URL = "http://gpbib.cs.ucl.ac.uk/gecco2004/LBP010.pdf",
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abstract = "Easy missions approaches to machine learning seek to
synthesise solutions for complex tasks from those for
simpler ones. In genetic programming, this has been
achieved by identifying goals and fitness functions for
subproblems of the overall problem. Solutions evolved
for these subproblems are then reused to speed up
learning, either as automatically defined functions
(ADFs) or by seeding a new GP population. Previous
positive results using both approaches for learning in
multi-agent systems (MAS) showed that incremental reuse
using easy missions achieves comparable or better
overall fitness than monolithic simple GP. A key
unresolved issue dealt with hybrid reuse using ADF plus
easy missions. Results in the keep-away soccer domain
(a test bed for MAS learning) were also inconclusive on
whether compactness inducing reuse helped or hurt
overall agent performance. In this paper, we compare
monolithic (simple GP and GP with ADFs) and easy
missions reuse to two types of GP learning systems with
incremental reuse: GP/ADF hybrids with easy missions
and single-mission incremental ADFs. As hypothesised,
pure easy missions reuse achieves results competitive
with the best hybrid approaches in this domain. We
interpret this finding and suggest a theoretical
approach to characterising incremental reuse and code
growth.",
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notes = "Part of \cite{keijzer:2004:GECCO:lbp}",
- }
Genetic Programming entries for
William H Hsu
Scott J Harmon
Edwin Rodriguez
Christopher Zhong
Citations