Genetic Network Programming with Rule Accumulation Considering Judgment Order
Created by W.Langdon from
gp-bibliography.bib Revision:1.8098
- @InProceedings{Wang:2009:cec,
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author = "Lutao Wang and Shingo Mabu and Fengming Ye and
Kotaro Hirasawa",
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title = "Genetic Network Programming with Rule Accumulation
Considering Judgment Order",
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booktitle = "2009 IEEE Congress on Evolutionary Computation",
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year = "2009",
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editor = "Andy Tyrrell",
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pages = "3176--3182",
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address = "Trondheim, Norway",
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month = "18-21 " # may,
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organization = "IEEE Computational Intelligence Society",
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publisher = "IEEE Press",
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isbn13 = "978-1-4244-2959-2",
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file = "P044.pdf",
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DOI = "doi:10.1109/CEC.2009.4983346",
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abstract = "Genetic Network Programming (GNP) is an evolutionary
algorithm derived form GA and GP. It can deal with
complex problems in dynamic environments efficiently
and effectively because of its directed graph
structure, reusability of nodes, and implicit memory
function. This paper proposed a new method to optimize
GNP algorithm by strengthening its exploitation ability
through extracting and using rules. In the former
research, the order of judgment node chain is ignored.
The basic idea of GNP with Rule Accumulation
Considering Judgment Order (GNP with RA) is to extract
rules with order having high fitness values from each
individual and store them in the pool every generation.
A rule is defined as a sequence of successive judgment
results and a processing node, which represents the
good experiences of the past behaviors. As a result,
the rule pool serves as an experience set of GNP
obtained in the evolution process. By extracting the
rules during the evolution period and then matching
them with the situations of the environment, we could
guide agents' behavior properly and get better
performance of the agents. In this paper, GNP with RA
is applied to the problem of determining agents'
behaviors and Tile-world was used as the simulation
environment in order to evaluate its effectiveness. The
simulation results demonstrate that GNP with RA could
have better performances than the conventional GNP
method both in the average fitness value and
stability.",
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keywords = "genetic algorithms, genetic programming, genetic
network programming",
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notes = "CEC 2009 - A joint meeting of the IEEE, the EPS and
the IET. IEEE Catalog Number: CFP09ICE-CDR. Also known
as \cite{4983346}",
- }
Genetic Programming entries for
Lutao Wang
Shingo Mabu
Fengming Ye
Kotaro Hirasawa
Citations