An initialization technique for geometric semantic GP based on demes evolution and despeciation
Created by W.Langdon from
gp-bibliography.bib Revision:1.8098
- @InProceedings{vanneschi:2017:CEC,
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author = "Leonardo Vanneschi and Illya Bakurov and
Mauro Castelli",
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booktitle = "2017 IEEE Congress on Evolutionary Computation (CEC)",
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title = "An initialization technique for geometric semantic GP
based on demes evolution and despeciation",
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year = "2017",
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editor = "Jose A. Lozano",
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pages = "113--120",
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address = "Donostia, San Sebastian, Spain",
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publisher = "IEEE",
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isbn13 = "978-1-5090-4601-0",
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abstract = "Initializing the population is a crucial step for
genetic programming, and several strategies have been
proposed so far. The issue is particularly important
for geometric semantic genetic programming, where
initialization is known to play a very important role.
In this paper, we propose an initialization technique
inspired by the biological phenomenon of demes
despeciation, i.e. the combination of demes of
previously distinct species into a new population. In
synthesis, the initial population of geometric semantic
genetic programming is created using the best
individuals of a set of separate subpopulations, or
demes, some of which run standard genetic programming
and the others geometric semantic genetic programming
for few generations. Geometric semantic genetic
programming with this novel initialization technique is
shown to outperform geometric semantic genetic
programming using the traditional ramped half-and-half
algorithm on six complex symbolic regression
applications. More specifically, on the studied
problems, the proposed initialization technique allows
us to generate solutions with comparable or even better
generalization ability, and of significantly smaller
size than the ramped half-and-half algorithm.",
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keywords = "genetic algorithms, genetic programming, regression
analysis, biological phenomenon, complex symbolic
regression applications, demes despeciation, demes
evolution, geometric semantic GP, initialization
technique, Evolution (biology), Semantics, Sociology,
Standards, Statistics",
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isbn13 = "978-1-5090-4601-0",
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DOI = "doi:10.1109/CEC.2017.7969303",
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month = "5-8 " # jun,
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notes = "IEEE Catalog Number: CFP17ICE-ART Also known as
\cite{7969303}",
- }
Genetic Programming entries for
Leonardo Vanneschi
Illya Bakurov
Mauro Castelli
Citations