Improving species distribution model quality with a parallel linear genetic programming-fuzzy algorithm

Created by W.Langdon from gp-bibliography.bib Revision:1.7615

@PhdThesis{MichelJanMarinusBieleveldCorr16,

• author = "Michel Jan Marinus Bieleveld",
• title = "Improving species distribution model quality with a parallel linear genetic programming-fuzzy algorithm",
• titletranslation = "Melhorar a qualidade de modelo de distribui{\c c}{\~a}o das esp{\'e}cies com um algoritmo paralelo de programa{\c c}{\~a}o linear gen{\'e}tico-fuzzy.",
• school = "Computer Engineering, Escola Politecnica",
• year = "2016",
• type = "Tese de Doutorado",
• address = "Brazil",
• month = "9 " # sep,
• keywords = "genetic algorithms, genetic programming, applied and specific algorithms, bioclimatologia, ecological niche models, fuzzy logic, species distribution modelling",
• bibsource = "OAI-PMH server at www.teses.usp.br",
• contributor = "Antonio Mauro Saraiva",
• language = "en",
• oai = "oai:teses.usp.br:tde-26012017-113329",
• rights = "Liberar o conte{\'u}do para acesso p{\'u}blico.",
• URL = "http://www.teses.usp.br/teses/disponiveis/3/3141/tde-26012017-113329/",
• URL = "http://www.teses.usp.br/teses/disponiveis/3/3141/tde-26012017-113329/en.php",
• URL = "http://www.teses.usp.br/teses/disponiveis/3/3141/tde-26012017-113329/publico/MichelJanMarinusBieleveldCorr16.pdf",
• publisher = "Biblioteca Digitais de Teses e Disserta{\c c}{\~o}es da USP",
• size = "142 pages",
• abstract = "Biodiversity, the variety of life on the planet, is declining due to climate change, population and species interactions and as the result f demographic and landscape dynamics. Integrated model-based assessments play a key role in understanding and exploring these complex dynamics and have proven use in conservation planning. Model-based assessments using Species Distribution Models constitute an efficient means of translating limited point data to distribution probability maps for current and future scenarios in support of conservation decision making. The aims of this doctoral study were to investigate; (1) the use of a hybrid genetic programming to build high quality models that handle noisy real-world presence and absence data, (2) the extension of this solution to exploit the parallelism inherent to genetic programming for fast scenario based decision making tasks, and (3) a conceptual framework to share models in the hope of enabling research synthesis. Subsequent to this, the quality of the method, evaluated with the true skill statistic, was examined with two case studies. The first with a dataset obtained by defining a virtual species, and the second with data extracted from the North American Breeding Bird Survey relating to mourning dove (Zenaida macroura). In these studies, the produced models effectively predicted the species distribution up to 30percent of error rate both presence and absence samples. The parallel implementation based on a twenty-node c3.xlarge Amazon EC2 StarCluster showed a linear speedup due to the multiple-deme coarse-grained design. The hybrid fuzzy genetic programming algorithm generated under certain consitions during the case studies significantly better transferable models.",
• abstract = "Biodiversidade, a variedade de vida no planeta, est{\'a} em decl{\'i}nio {\`a}s altera{\c c}{\~o}es clim{\'a}ticas, mudan{\c c}as nas intera{\c c}{\~o}es das popula{\c c}{\~o}es e esp{\'e}cies, bem como nas altera{\c c}{\~o}es demogr{\'a}ficas e na din{\^a}mica de paisagens. Avalia{\c c}{\~o}es integradas baseadas em modelo desempenham um papel fundamental na compreens{\~a}o e na explora{\c c}{\~a}o destas din{\^a}micas complexas e tem o seu uso comprovado no planejamento de conserva{\c c}{\~a}o da biodiversidade. Os objetivos deste estudo de doutorado foram investigar; (1) o uso de t{\'e}cnicas de programa{\c c}{\~a}o gen{\'e}tica e fuzzy para construir modelos de alta qualidade que lida com presen{\c c}a e aus{\^e}ncia de dados ruidosos do mundo real, (2) a extens{\~a}o desta solu{\c c}{\~a}o para explorar o paralelismo inerente {\`a} programa{\c c}{\~a}o gen{\'e}tica para acelerar tomadas de decis{\~a}o e (3) um framework conceitual para compartilhar modelos, na expectativa de permitir a s{\'i}ntese de pesquisa. Subsequentemente, a qualidade do m{\'e}todo, avaliada com a true skill statistic, foi examinado com dois estudos de caso. O primeiro utilizou um conjunto de dados fict{\'i}cios obtidos a partir da defini{\c c}{\~a}o de uma esp{\'e}cie virtual, e o segundo utilizou dados de uma esp{\'e}cie de pomba (Zenaida macroura) obtidos do North American Breeding Bird Survey. Nestes estudos, os modelos foram capazes de predizer a distribui{\c c}{\~a}o das esp{\'e}cies maneira correta mesmo utilizando bases de dados com at{\'e} 30percent de erros nas amostras de presen{\c c}a e de aus{\^e}ncia. A implementa{\c c}{\~a}o paralela utilizando um cluster de vinte n{\'o}s c3.xlarge Amazon EC2 StarCluster, mostrou uma acelera{\c c}{\~a}o linear devido ao arquitetura de m{\'u}ltiplos deme de granula{\c c}{\~a}o grossa. O algoritmo de programa{\c c}{\~a}o gen{\'e}tica e fuzzy gerada em determinadas condi{\c c}{\~o}es durante os estudos de caso, foram significativamente melhores na transfer{\^e}ncia do que os algoritmos do BIOMOD.",

}

Genetic Programming entries for Michel Jan Marinus Bieleveld

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