Adaptive Bi-objective Genetic Programming for Data-Driven System Modeling

Created by W.Langdon from gp-bibliography.bib Revision:1.3963

@InProceedings{conf/icic/BevilacquaNMI16,
  title =        "Adaptive Bi-objective Genetic Programming for
                 Data-Driven System Modeling",
  author =       "Vitoantonio Bevilacqua and Nicola Nuzzolese and 
                 Ernesto Mininno and Giovanni Iacca",
  bibdate =      "2017-05-23",
  bibsource =    "DBLP,
                 http://dblp.uni-trier.de/db/conf/icic/icic2016-3.html#BevilacquaNMI16",
  booktitle =    "Intelligent Computing Methodologies - 12th
                 International Conference, {ICIC} 2016, Lanzhou, China,
                 August 2-5, 2016, Proceedings, Part {III}",
  publisher =    "Springer",
  year =         "2016",
  volume =       "9773",
  editor =       "De-Shuang Huang and Kyungsook Han and Abir Hussain",
  isbn13 =       "978-3-319-42296-1",
  pages =        "248--259",
  series =       "Lecture Notes in Computer Science",
  keywords =     "genetic algorithms, genetic programming,
                 multi-objective evolutionary algorithms, adaptive
                 genetic programming, machine learning, home automation,
                 energy efficiency",
  URL =          "https://link.springer.com/chapter/10.1007%2F978-3-319-42297-8_24",
  DOI =          "doi:10.1007/978-3-319-42297-8_24",
  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