A Comparison of Hybrid Incremental Reuse Strategies for Reinforcement Learning in Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.8120

@InProceedings{Harmon:ACo:gecco2004,

• author = "Scott Harmon and Edwin Rodriguez and Christopher Zhong and William Hsu",
• title = "A Comparison of Hybrid Incremental Reuse Strategies for Reinforcement Learning in Genetic Programming",
• booktitle = "Genetic and Evolutionary Computation -- GECCO-2004, Part II",
• year = "2004",
• 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",
• series = "Lecture Notes in Computer Science",
• pages = "706--707",
• address = "Seattle, WA, USA",
• publisher_address = "Heidelberg",
• month = "26-30 " # jun,
• organisation = "ISGEC",
• publisher = "Springer-Verlag",
• volume = "3103",
• keywords = "genetic algorithms, genetic programming: Poster",
• ISBN = "3-540-22343-6",
• ISSN = "0302-9743",
• annote = "The Pennsylvania State University CiteSeerX Archives",
• bibsource = "OAI-PMH server at citeseerx.ist.psu.edu",
• language = "en",
• oai = "oai:CiteSeerX.psu:10.1.1.1038.994",
• rights = "Metadata may be used without restrictions as long as the oai identifier remains attached to it.",
• URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.1038.994",
• URL = "http://dynamics.org/Altenberg/UH_ICS/EC_REFS/GP_REFS/GECCO/2004/31030706.pdf",
• DOI = "doi:10.1007/b98645",
• DOI = "doi:10.1007/978-3-540-24855-2_79",
• size = "2",
• 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.",
• 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