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.
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 - http://www.scopus.com/inward/record.url?scp=85044212324&partnerID=8YFLogxK
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 -
