Gene expression programming with a local search operator
Created by W.Langdon from
gp-bibliography.bib Revision:1.8098
- @InProceedings{Safavi:2017:AISP,
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author = "Asghar Amir Safavi and Manoochehr Kelarestaghi and
Farshad Eshghi",
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booktitle = "2017 Artificial Intelligence and Signal Processing
Conference (AISP)",
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title = "Gene expression programming with a local search
operator",
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year = "2017",
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pages = "53--58",
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abstract = "Gene expression programming (GEP) is one of the newest
evolutionary algorithms, the linear model of genetic
programming that have been much attention to it, in
recent years. In this article this algorithm and
memetic algorithms are discussed. Here we are tried to
improve its efficiency by combining gene expression
programming with a local search method. The proposed
algorithm called GEP-LS and it is applicable for all
problems in the field of evolutionary computation.
Random Mutation Hill-Climbing (RMHC) and Simulated
Annealing (SA) methods are separately used to implement
local search and their results are compared with each
other. Finally, a comparison with the conventional gene
expression programming algorithm is performed. These
comparisons is performed on problems of symbolic
regression, sequence induction with constants creation
and robotic planning. The results show that performance
of the proposed algorithm with RMHC method is
relatively better than other algorithms and is able to
solve all problems used here with higher accuracy and
lower error.",
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keywords = "genetic algorithms, genetic programming, Gene
expression programming",
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DOI = "doi:10.1109/AISP.2017.8324106",
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month = oct,
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notes = "Also known as \cite{8324106}",
- }
Genetic Programming entries for
Asghar Amir Safavi
Manoochehr Kelarestaghi
Farshad Eshghi
Citations