A Comparison of Hybrid Incremental Reuse Strategies for Reinforcement Learning in Genetic Programming
Created by W.Langdon from
gp-bibliography.bib Revision:1.8081
- @InProceedings{Harmon:ACo:gecco2004,
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author = "Scott Harmon and Edwin Rodriguez and
Christopher Zhong and William Hsu",
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title = "A Comparison of Hybrid Incremental Reuse Strategies
for Reinforcement Learning in Genetic Programming",
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booktitle = "Genetic and Evolutionary Computation -- GECCO-2004,
Part II",
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year = "2004",
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editor = "Kalyanmoy Deb and Riccardo Poli and
Wolfgang Banzhaf and Hans-Georg Beyer and Edmund Burke and
Paul Darwen and Dipankar Dasgupta and Dario Floreano and
James Foster and Mark Harman and Owen Holland and
Pier Luca Lanzi and Lee Spector and Andrea Tettamanzi and
Dirk Thierens and Andy Tyrrell",
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series = "Lecture Notes in Computer Science",
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pages = "706--707",
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address = "Seattle, WA, USA",
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publisher_address = "Heidelberg",
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month = "26-30 " # jun,
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organisation = "ISGEC",
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publisher = "Springer-Verlag",
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volume = "3103",
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keywords = "genetic algorithms, genetic programming: Poster",
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ISBN = "3-540-22343-6",
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ISSN = "0302-9743",
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annote = "The Pennsylvania State University CiteSeerX Archives",
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bibsource = "OAI-PMH server at citeseerx.ist.psu.edu",
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language = "en",
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oai = "oai:CiteSeerX.psu:10.1.1.1038.994",
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rights = "Metadata may be used without restrictions as long as
the oai identifier remains attached to it.",
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URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.1038.994",
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URL = "http://dynamics.org/Altenberg/UH_ICS/EC_REFS/GP_REFS/GECCO/2004/31030706.pdf",
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DOI = "doi:10.1007/b98645",
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DOI = "doi:10.1007/978-3-540-24855-2_79",
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size = "2",
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abstract = "Easy missions is an approach to machine learning that
seeks to synthesize solutions for complex tasks from
those for simpler ones. ISLES (Incrementally Staged
Learning from Easier Subtasks) [1] is a genetic
programming (GP) technique that achieves this by using
identified 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 (ADF) 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 single-layered GP. A key unresolved issue dealt
with hybrid reuse using ADF with easy missions. Results
in the keep-away soccer (KAS) [2] 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 reuse using
single-layered (with and without ADF) GP and easy
missions GPs to two new types of GP learning systems
with incremental reuse.",
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notes = "GECCO-2004 A joint meeting of the thirteenth
international conference on genetic algorithms
(ICGA-2004) and the ninth annual genetic programming
conference (GP-2004)",
- }
Genetic Programming entries for
Scott J Harmon
Edwin Rodriguez
Christopher Zhong
William H Hsu
Citations