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

Created by W.Langdon from gp-bibliography.bib Revision:1.7630

@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