Adaptive Bi-objective Genetic Programming for Data-Driven System Modeling
Created by W.Langdon from
gp-bibliography.bib Revision:1.8081
- @InProceedings{conf/icic/BevilacquaNMI16,
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title = "Adaptive Bi-objective Genetic Programming for
Data-Driven System Modeling",
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author = "Vitoantonio Bevilacqua and Nicola Nuzzolese and
Ernesto Mininno and Giovanni Iacca",
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bibdate = "2017-05-23",
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bibsource = "DBLP,
http://dblp.uni-trier.de/db/conf/icic/icic2016-3.html#BevilacquaNMI16",
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booktitle = "Intelligent Computing Methodologies - 12th
International Conference, {ICIC} 2016, Lanzhou, China,
August 2-5, 2016, Proceedings, Part {III}",
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publisher = "Springer",
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year = "2016",
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volume = "9773",
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editor = "De-Shuang Huang and Kyungsook Han and Abir Hussain",
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isbn13 = "978-3-319-42296-1",
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pages = "248--259",
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series = "Lecture Notes in Computer Science",
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keywords = "genetic algorithms, genetic programming,
multi-objective evolutionary algorithms, adaptive
genetic programming, machine learning, home automation,
energy efficiency",
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URL = "https://link.springer.com/chapter/10.1007%2F978-3-319-42297-8_24",
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DOI = "doi:10.1007/978-3-319-42297-8_24",
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abstract = "We propose in this paper a modification of one of the
modern state-of-the-art genetic programming algorithms
used for data-driven modelling, namely the Bi-objective
Genetic Programming (BioGP). The original method is
based on a concurrent minimization of both the training
error and complexity of multiple candidate models
encoded as Genetic Programming trees. Also, BioGP is
empowered by a predator-prey co-evolutionary model
where virtual predators are used to suppress solutions
(preys) characterised by a poor trade-off error vs
complexity. In this work, we incorporate in the
original BioGP an adaptive mechanism that automatically
tunes the mutation rate, based on a characterisation of
the current population (in terms of entropy) and on the
information that can be extracted from it. We show
through numerical experiments on two different datasets
from the energy domain that the proposed method, named
BioAGP (where A stands for Adaptive), performs better
than the original BioGP, allowing the search to
maintain a good diversity level in the population,
without affecting the convergence rate.",
- }
Genetic Programming entries for
Vitoantonio Bevilacqua
Nicola Nuzzolese
Ernesto Mininno
Giovanni Iacca
Citations