Surrogate Genetic Programming: A semantic aware evolutionary search
Created by W.Langdon from
gp-bibliography.bib Revision:1.7954
- @Article{Kattan:2015:ISa,
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author = "Ahmed Kattan and Yew-Soon Ong",
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title = "Surrogate Genetic Programming: A semantic aware
evolutionary search",
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journal = "Information Sciences",
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volume = "296",
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pages = "345--359",
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year = "2015",
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ISSN = "0020-0255",
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DOI = "doi:10.1016/j.ins.2014.10.053",
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URL = "http://www.sciencedirect.com/science/article/pii/S0020025514010421",
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abstract = "Many semantic search based on Genetic Programming (GP)
use a trial-and-error scheme to attain semantically
diverse offspring in the evolutionary search. This
results in significant impediments on the success of
semantic-based GP in solving real world problems, due
to the additional computational overheads incurred.
This paper proposes a surrogate Genetic Programming (or
sGP in short) to retain the appeal of semantic-based
evolutionary search for handling challenging problems
with enhanced efficiency. The proposed sGP divides the
population into two parts (mu and lambda) then it
evolves mu percentage of the population using standard
GP search operators, while the remaining lambda
percentage of the population are evolved with the aid
of meta-models (or approximation models) that serve as
surrogate to the original objective function evaluation
(which is computationally intensive). In contrast to
previous works, two forms of meta-models are introduced
in this study to make the idea of using surrogate in GP
search feasible and successful. The first denotes a
{"}Semantic-model{"} for prototyping the semantic
representation space of the GP trees
(genotype/syntactic-space). The second is a
{"}Fitness-model{"}, which maps solutions in the
semantic space to the objective or fitness space. By
exploiting the two meta-models collectively in serving
as a surrogate that replaces the original problem
landscape of the GP search process, more cost-effective
generation of offspring that guides the search in
exploring regions where high quality solutions resides
can then be attained. Experimental studies covering
three separate GP domains, namely, (1) Symbolic
regression, (2) Even n-parity bit, and (3) a real-world
Time-series forecasting problem domain involving three
datasets, demonstrate that sGP is capable of attaining
reliable, high quality, and efficient performance under
a limited computational budget. Results also showed
that sGP outperformed the standard GP, GP based on
random training-set technique, and GP based on
conventional data-centric objectives as surrogate.",
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keywords = "genetic algorithms, genetic programming, Semantic
space, Surrogate model, Semantic-model, Fitness-model,
sGP",
- }
Genetic Programming entries for
Ahmed Kattan
Yew-Soon Ong
Citations