Grammar Model-based Program Evolution
Created by W.Langdon from
gp-bibliography.bib Revision:1.8098
- @InProceedings{shan:2004:gmpe,
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author = "Yin Shan and Robert I. McKay and Rohan Baxter and
Hussein Abbass and Daryl Essam and Nguyen Xuan Hoai",
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title = "Grammar Model-based Program Evolution",
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booktitle = "Proceedings of the 2004 IEEE Congress on Evolutionary
Computation",
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pages = "478--485",
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year = "2004",
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publisher = "IEEE Press",
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month = "20-23 " # jun,
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address = "Portland, Oregon, USA",
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ISBN = "0-7803-8515-2",
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keywords = "genetic algorithms, genetic programming, Theory of
evolutionary algorithms",
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URL = "http://sc.snu.ac.kr/courses/2006/fall/pg/aai/GP/shan/scfgcec04.pdf",
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DOI = "doi:10.1109/CEC.2004.1330895",
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size = "8 pages",
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abstract = "In Evolutionary Computation, fixed genetic operators
may destroy the sub-solution, usually called building
blocks, instead of discovering and preserving them. One
way to overcome this problem is to build a model based
on the good individuals, and sample this model to
obtain the next population. In this paper, along this
line, we propose a new method, Grammar Model-based
Program Evolution (GMPE) to evolved GP program. We
replace common GP genetic operator with a Probabilistic
Context-free Grammar (SCFG). In each generation, an
SCFG is learnt, and a new population is generated by
sampling this SCFG model. On two benchmark problems we
have studied, GMPE significantly outperforms
conventional GP, learning faster and more reliably.",
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notes = "CEC 2004 - A joint meeting of the IEEE, the EPS, and
the IEE.",
- }
Genetic Programming entries for
Yin Shan
R I (Bob) McKay
Rohan Baxter
Hussein A Abbass
Daryl Essam
Nguyen Xuan Hoai
Citations