Artificial bee colony programming for symbolic regression
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- @Article{Karaboga20121,
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author = "Dervis Karaboga and Celal Ozturk and
Nurhan Karaboga and Beyza Gorkemli",
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title = "Artificial bee colony programming for symbolic
regression",
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journal = "Information Sciences",
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volume = "209",
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pages = "1--15",
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year = "2012",
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ISSN = "0020-0255",
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DOI = "doi:10.1016/j.ins.2012.05.002",
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URL = "http://www.sciencedirect.com/science/article/pii/S0020025512003295",
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keywords = "genetic algorithms, genetic programming, Symbolic
regression, Artificial bee colony algorithm, Artificial
bee colony programming",
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abstract = "Artificial bee colony algorithm simulating the
intelligent foraging behaviour of honey bee swarms is
one of the most popular swarm based optimisation
algorithms. It has been introduced in 2005 and applied
in several fields to solve different problems up to
date. In this paper, an artificial bee colony
algorithm, called as Artificial Bee Colony Programming
(ABCP), is described for the first time as a new method
on symbolic regression which is a very important
practical problem. Symbolic regression is a process of
obtaining a mathematical model using given finite
sampling of values of independent variables and
associated values of dependent variables. In this work,
a set of symbolic regression benchmark problems are
solved using artificial bee colony programming and then
its performance is compared with the very well-known
method evolving computer programs, genetic programming.
The simulation results indicate that the proposed
method is very feasible and robust on the considered
test problems of symbolic regression.",
- }
Genetic Programming entries for
Dervis Karaboga
Celal Ozturk
Nurhan Karaboga
Beyza Gorkemli
Citations