Scaling Genetic Programming for Data Classification using MapReduce Methodology

Created by W.Langdon from gp-bibliography.bib Revision:1.8129

@InProceedings{Al-Madi:2013:nabic,

• author = "Nailah Al-Madi and Simone A. Ludwig",
• title = "Scaling Genetic Programming for Data Classification using {MapReduce} Methodology",
• booktitle = "5th World Congress on Nature and Biologically Inspired Computing",
• year = "2013",
• editor = "Simone Ludwig and Patricia Melin and Ajith Abraham and Ana Maria Madureira and Kendall Nygard and Oscar Castillo and Azah Kamilah Muda and Kun Ma and Emilio Corchado",
• pages = "132--139",
• address = "Fargo, USA",
• month = "12-14 " # aug,
• publisher = "IEEE",
• keywords = "genetic algorithms, genetic programming, Evolutionary computation, data classification, Parallel Processing, MapReduce, Hadoop",
• isbn13 = "978-1-4799-1415-9",
• URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/al-madi/MRGP.pdf",
• URL = "http://www.mirlabs.net/nabic13/proceedings/html/paper34.xml",
• DOI = "doi:10.1109/NaBIC.2013.6617851",
• size = "8 pages",
• abstract = "Genetic Programming (GP) is an optimisation method that has proved to achieve good results. It solves problems by generating programs and applying natural operations on these programs until a good solution is found. GP has been used to solve many classifications problems, however, its drawback is the long execution time. When GP is applied on the classification task, the execution time proportionally increases with the dataset size. Therefore, to manage the long execution time, the GP algorithm is parallelised in order to speed up the classification process. Our GP is implemented based on the MapReduce methodology (abbreviated as MRGP), in order to benefit from the MapReduce concept in terms of fault tolerance, load balancing, and data locality. MRGP does not only accelerate the execution time of GP for large datasets, it also provides the ability to use large population sizes, thus finding the best result in fewer numbers of generations. MRGP is evaluated using different population sizes ranging from 1,000 to 100,000 measuring the accuracy, scalability, and speedup",
• notes = "USB only?, IEEE Catalog Number: CFP1395H-POD Also known as \cite{6617851}",

}

Genetic Programming entries for Nailah Al-Madi Simone A Ludwig

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