Learning and Evolution of Genetic Network Programming with Knowledge Transfer
Created by W.Langdon from
gp-bibliography.bib Revision:1.8098
- @InProceedings{Li:2014:CECd,
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title = "Learning and Evolution of Genetic Network Programming
with Knowledge Transfer",
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author = "Xianneng Li and Wen He and Kotaro Hirasawa",
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pages = "798--805",
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booktitle = "Proceedings of the 2014 IEEE Congress on Evolutionary
Computation",
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year = "2014",
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month = "6-11 " # jul,
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editor = "Carlos A. {Coello Coello}",
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address = "Beijing, China",
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ISBN = "0-7803-8515-2",
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keywords = "genetic algorithms, genetic programming, genetic
network programming, Representation and operators,
Adaptive dynamic programming and reinforcement
learning",
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DOI = "doi:10.1109/CEC.2014.6900315",
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abstract = "Traditional evolutionary algorithms (EAs) generally
starts evolution from scratch, in other words,
randomly. However, this is computationally consuming,
and can easily cause the instability of evolution. In
order to solve the above problems, this paper describes
a new method to improve the evolution efficiency of a
recently proposed graph-based EA genetic network
programming (GNP) by introducing knowledge transfer
ability. The basic concept of the proposed method,
named GNP-KT, arises from two steps: First, it
formulates the knowledge by discovering abstract
decision-making rules from source domains in a learning
classifier system (LCS) aspect; Second, the knowledge
is adaptively reused as advice when applying GNP to a
target domain. A reinforcement learning (RL)-based
method is proposed to automatically transfer knowledge
from source domain to target domain, which eventually
allows GNP-KT to result in better initial performance
and final fitness values. The experimental results in a
real mobile robot control problem confirm the
superiority of GNP-KT over traditional methods.",
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notes = "WCCI2014",
- }
Genetic Programming entries for
Xianneng Li
Wen He
Kotaro Hirasawa
Citations