The influence of population size in geometric semantic GP
Created by W.Langdon from
gp-bibliography.bib Revision:1.8051
- @Article{Castelli:2017:SEC,
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author = "Mauro Castelli and Luca Manzoni and Sara Silva and
Leonardo Vanneschi and Ales Popovic",
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title = "The influence of population size in geometric semantic
{GP}",
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journal = "Swarm and Evolutionary Computation",
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volume = "32",
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pages = "110--120",
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year = "2017",
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ISSN = "2210-6502",
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DOI = "doi:10.1016/j.swevo.2016.05.004",
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URL = "http://www.sciencedirect.com/science/article/pii/S2210650216300256",
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abstract = "In this work, we study the influence of the population
size on the learning ability of Geometric Semantic
Genetic Programming for the task of symbolic
regression. A large set of experiments, considering
different population size values on different
regression problems, has been performed. Results show
that, on real-life problems, having small populations
results in a better training fitness with respect to
the use of large populations after the same number of
fitness evaluations. However, performance on the test
instances varies among the different problems: in
datasets with a high number of features, models
obtained with large populations present a better
performance on unseen data, while in datasets
characterized by a relative small number of variables a
better generalization ability is achieved by using
small population size values. When synthetic problems
are taken into account, large population size values
represent the best option for achieving good quality
solutions on both training and test instances.",
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keywords = "genetic algorithms, genetic programming, Semantics,
Population size",
- }
Genetic Programming entries for
Mauro Castelli
Luca Manzoni
Sara Silva
Leonardo Vanneschi
Ales Popovic
Citations