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

doi:10.1109/IRI.2017.93

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

Information Systems

Research output: A Conference proceeding or a Chapter in Book › Conference 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 language	English
Title of host publication	Proceedings - 2017 IEEE International Conference on Information Reuse and Integration, IRI 2017
Place of Publication	San Diego, US
Publisher	IEEE, Institute of Electrical and Electronics Engineers
Pages	162-170
Number of pages	9
Volume	2017-January
ISBN (Electronic)	9781538615621
ISBN (Print)	9781538615638
DOIs	https://doi.org/10.1109/IRI.2017.93
Publication status	Published - 4 Aug 2017
Event	18th IEEE International Conference on Information Reuse and Integration - San Diego, United States Duration: 4 Aug 2017 → 6 Aug 2017

Publication series

Name	Proceedings - 2017 IEEE International Conference on Information Reuse and Integration, IRI 2017
Volume	2017-January

Conference

Conference	18th IEEE International Conference on Information Reuse and Integration
Abbreviated title	IRI 2017
Country/Territory	United States
City	San Diego
Period	4/08/17 → 6/08/17

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 13 Climate Action

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10.1109/IRI.2017.93

Cite this

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). Article 8102933 (Proceedings - 2017 IEEE International Conference on Information Reuse and Integration, IRI 2017; Vol. 2017-January). IEEE, Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/IRI.2017.93

Santana, Fabiana ; Bravo Pariente, César Alberto ; Saraiva, Antonio Mauro. / Species distribution modeling with scalability: The case study of P-GARP, a parallel genetic algorithm for rule-set production. 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, 2017. pp. 162-170 (Proceedings - 2017 IEEE International Conference on Information Reuse and Integration, IRI 2017).

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

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.",

keywords = "Ecological niche modeling, GARP, Genetic algorithms, Parallel algorithms, PGARP, Species distribution modeling",

author = "Fabiana Santana and \{Bravo Pariente\}, \{C{\'e}sar Alberto\} and Saraiva, \{Antonio Mauro\}",

note = "Funding Information: The authors are grateful to the Faculty of Education, Science, Technology, and Mathematics (ESTEM), University of Canberra for the financial support. The authors are grateful to FAPESP, the State of S?o Paulo Research Foundation, Brazil, for the support to the openModeller project, under grant 04/11012-0. Funding Information: The authors are grateful to the Faculty of Education, Science, Technology, and Mathematics (ESTEM), University of Canberra for the financial support. The authors are grateful to FAPESP, the State of S{\~a}o Paulo Research Foundation, Brazil, for the support to the openModeller project, under grant 04/11012-0. Publisher Copyright: {\textcopyright} 2017 IEEE. Funding Information: The authors are grateful to the Faculty of Education, Science, Technology, and Mathematics (ESTEM), University of Canberra for the financial support. The authors are grateful to FAPESP, the State of S{\~a}o Paulo Research Foundation, Brazil, for the support to the openModeller project, under grant 04/11012-0. Publisher Copyright: {\textcopyright} 2017 IEEE.; 18th IEEE International Conference on Information Reuse and Integration, IRI 2017 ; Conference date: 04-08-2017 Through 06-08-2017",

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Santana, F, Bravo Pariente, CA & Saraiva, AM 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, 8102933, Proceedings - 2017 IEEE International Conference on Information Reuse and Integration, IRI 2017, vol. 2017-January, IEEE, Institute of Electrical and Electronics Engineers, San Diego, US, pp. 162-170, 18th IEEE International Conference on Information Reuse and Integration, San Diego, United States, 4/08/17. https://doi.org/10.1109/IRI.2017.93

Species distribution modeling with scalability: The case study of P-GARP, a parallel genetic algorithm for rule-set production. / Santana, Fabiana; Bravo Pariente, César Alberto; Saraiva, Antonio Mauro.
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, 2017. p. 162-170 8102933 (Proceedings - 2017 IEEE International Conference on Information Reuse and Integration, IRI 2017; Vol. 2017-January).

Research output: A Conference proceeding or a Chapter in Book › Conference contribution

TY - GEN

T1 - Species distribution modeling with scalability: The case study of P-GARP, a parallel genetic algorithm for rule-set production

AU - Santana, Fabiana

AU - Bravo Pariente, César Alberto

AU - Saraiva, Antonio Mauro

N1 - Funding Information: The authors are grateful to the Faculty of Education, Science, Technology, and Mathematics (ESTEM), University of Canberra for the financial support. The authors are grateful to FAPESP, the State of S?o Paulo Research Foundation, Brazil, for the support to the openModeller project, under grant 04/11012-0. Funding Information: The authors are grateful to the Faculty of Education, Science, Technology, and Mathematics (ESTEM), University of Canberra for the financial support. The authors are grateful to FAPESP, the State of São Paulo Research Foundation, Brazil, for the support to the openModeller project, under grant 04/11012-0. Publisher Copyright: © 2017 IEEE. Funding Information: The authors are grateful to the Faculty of Education, Science, Technology, and Mathematics (ESTEM), University of Canberra for the financial support. The authors are grateful to FAPESP, the State of São Paulo Research Foundation, Brazil, for the support to the openModeller project, under grant 04/11012-0. Publisher Copyright: © 2017 IEEE.

PY - 2017/8/4

Y1 - 2017/8/4

N2 - 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.

AB - 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.

KW - Ecological niche modeling

KW - GARP

KW - Genetic algorithms

KW - Parallel algorithms

KW - PGARP

KW - Species distribution modeling

UR - https://www.scopus.com/pages/publications/85044212324

UR - http://www.mendeley.com/research/species-distribution-modeling-scalability-case-study-pgarp-parallel-genetic-algorithm-ruleset-produc

U2 - 10.1109/IRI.2017.93

DO - 10.1109/IRI.2017.93

M3 - Conference contribution

AN - SCOPUS:85044212324

SN - 9781538615638

VL - 2017-January

T3 - Proceedings - 2017 IEEE International Conference on Information Reuse and Integration, IRI 2017

SP - 162

EP - 170

BT - Proceedings - 2017 IEEE International Conference on Information Reuse and Integration, IRI 2017

PB - IEEE, Institute of Electrical and Electronics Engineers

CY - San Diego, US

T2 - 18th IEEE International Conference on Information Reuse and Integration

Y2 - 4 August 2017 through 6 August 2017

ER -

Santana F, Bravo Pariente CA, Saraiva AM. 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. San Diego, US: IEEE, Institute of Electrical and Electronics Engineers. 2017. p. 162-170. 8102933. (Proceedings - 2017 IEEE International Conference on Information Reuse and Integration, IRI 2017). doi: 10.1109/IRI.2017.93

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

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