A Population Diversity-Oriented Gene Expression Programming for Function Finding
Created by W.Langdon from
gp-bibliography.bib Revision:1.8081
- @InProceedings{conf/seal/LiuLLJ10,
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title = "A Population Diversity-Oriented Gene Expression
Programming for Function Finding",
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author = "Ruochen Liu and Qifeng Lei and Jing Liu and
Licheng Jiao",
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booktitle = "8th International Conference on Simulated Evolution
and Learning (SEAL 2010)",
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year = "2010",
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volume = "6457",
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editor = "Kalyanmoy Deb and Arnab Bhattacharya and
Nirupam Chakraborti and Partha Chakroborty and Swagatam Das and
Joydeep Dutta and Santosh K. Gupta and Ashu Jain and
Varun Aggarwal and J{\"u}rgen Branke and
Sushil J. Louis and Kay Chen Tan",
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series = "Lecture Notes in Computer Science",
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pages = "215--219",
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address = "Kanpur, India",
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month = dec # " 1-4",
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publisher = "Springer",
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keywords = "genetic algorithms, genetic programming, gene
expression programming",
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bibdate = "2010-12-01",
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bibsource = "DBLP,
http://dblp.uni-trier.de/db/conf/seal/seal2010.html#LiuLLJ10",
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isbn13 = "978-3-642-17297-7",
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DOI = "doi:10.1007/978-3-642-17298-4",
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abstract = "Gene expression programming (GEP) is a novel
evolutionary algorithm, which combines the advantages
of simple genetic algorithm (SGA) and genetic
programming (GP). Owing to its special structure of
linear encoding and nonlinear decoding, GEP has been
applied in various fields such as function finding and
data classification. In this paper, we propose a
modified GEP (Mod-GEP), in which, two strategies
including population updating and population pruning
are used to increase the diversity of population.
Mod-GEP is applied into two practical function finding
problems, the results show that Mod-GEP can get a more
satisfactory solution than that of GP, GEP and GEP
based on statistical analysis and stagnancy (AMACGEP",
- }
Genetic Programming entries for
Ruochen Liu
Qifeng Lei
Jing Liu
Licheng Jiao
Citations