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A (\mu + \lambda) - GP Algorithm and its use for Regression Problems
Arlington, Virginia November 13-November 15
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/ICTAI.2006.618th IEEE International Conference on ...
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Eduardo Oliveira Costa, Aurora Pozo, "A (\mu + \lambda) - GP Algorithm and its use for Regression Problems," Tools with Artificial Intelligence, IEEE International Conference on, pp. 10-17, 18th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'06), 2006.
 
 
BibTex
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@article{ 10.1109/ICTAI.2006.6,
author = {Eduardo Oliveira Costa and Aurora Pozo},
title = {A (\mu + \lambda) - GP Algorithm and its use for Regression Problems},
journal ={Tools with Artificial Intelligence, IEEE International Conference on},
volume = {0},
year = {2006},
issn = {1082-3409},
pages = {10-17},
doi = {http://doi.ieeecomputersociety.org/10.1109/ICTAI.2006.6},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
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TY - CONF
JO - Tools with Artificial Intelligence, IEEE International Conference on
TI - A (\mu + \lambda) - GP Algorithm and its use for Regression Problems
SN - 1082-3409
SP10
EP17
A1 - Eduardo Oliveira Costa,
A1 - Aurora Pozo,
PY - 2006
KW - null
VL - 0
JA - Tools with Artificial Intelligence, IEEE International Conference on
ER -
 
Eduardo Oliveira Costa, Federal University of Parana (UFPR), Brazil
Aurora Pozo, Federal University of Parana (UFPR), Brazil
The Genetic Programming (GP) is a powerful technique for Symbolic Regression. However, because it is a new area, many improvements can be obtained changing the basic behavior of the method. In this way, this work develop a different Genetic Programming algorithm doing some modifications on the classical GP algorithm and adding some concepts of Evolution Strategies. The new approach was evaluated using two instances of Symbolic Regression problem -- the Binomial--3 problem (a tunably difficult problem), proposed in [4] and the problem of Modelling Software Reliability Growth (an application of Symbolic Regression). The discovered results were compared with the classical GP algorithm. The Symbolic Regression problems obtained excellent results and an improvement was detected using the proposed approach.
Citation:
Eduardo Oliveira Costa, Aurora Pozo, "A (\mu + \lambda) - GP Algorithm and its use for Regression Problems," ictai, pp.10-17, 18th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'06), 2006
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