Species distribution modeling with scalability: The case study of P-GARP, a parallel genetic algorithm for rule-set production

Fabiana Santana, César Alberto Bravo Pariente, Antonio Mauro Saraiva

Research output: A Conference proceeding or a Chapter in BookConference contribution

1 Citation (Scopus)

Abstract

Species distribution modeling (SDM) calculates a species' probabilistic distribution by combining Environmental raster layers with species datasets. Such models can help to answer complex questions in Ecology/Biology/Health, e.g., by calculating impacts of climate changes in Biodiversity, or the potential for a disease spread (vectors' modeling). Machine learning is largely applied in SDM, being the Genetic Algorithm for Rule-set Production (GARP) one of the most reliable solutions. However, GARP's convergence needs to speedup under certain conditions (high resolution or number of layers), for which this paper proposes P-GARP, a parallel, scalable implementation of GARP. P-GARP was implemented onto a SGI Altix XE 1300 cluster with 2 quad-core processors/node. Preliminary results show an expressive 3.2/node speedup. Premature convergence is not observed in PGARP and its accuracy is very similar to GARP´s. Effective solutions to improve this speedup in even larger scale are proposed, along with a discussion about P-GARP correctness and efficiency.

Original languageEnglish
Title of host publicationProceedings - 2017 IEEE International Conference on Information Reuse and Integration, IRI 2017
Place of PublicationSan Diego, US
PublisherIEEE, Institute of Electrical and Electronics Engineers
Pages162-170
Number of pages9
Volume2017-January
ISBN (Electronic)9781538615621
ISBN (Print)9781538615638
DOIs
Publication statusPublished - 4 Aug 2017
Event18th IEEE International Conference on Information Reuse and Integration - San Diego, United States
Duration: 4 Aug 20176 Aug 2017

Publication series

NameProceedings - 2017 IEEE International Conference on Information Reuse and Integration, IRI 2017
Volume2017-January

Conference

Conference18th IEEE International Conference on Information Reuse and Integration
Abbreviated titleIRI 2017
CountryUnited States
CitySan Diego
Period4/08/176/08/17

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Santana, F., Bravo Pariente, C. A., & Saraiva, A. M. (2017). Species distribution modeling with scalability: The case study of P-GARP, a parallel genetic algorithm for rule-set production. In Proceedings - 2017 IEEE International Conference on Information Reuse and Integration, IRI 2017 (Vol. 2017-January, pp. 162-170). [8102933] (Proceedings - 2017 IEEE International Conference on Information Reuse and Integration, IRI 2017; Vol. 2017-January). San Diego, US: IEEE, Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/IRI.2017.93