Vol. 2 Issue 4
Year: 2015
Issue:Aug-Oct
Title:Genetic Algorithm Using MapReduce-A Critical Review
Author Name:Palak Sachar, and Vikas Khullar
Synopsis:
Now-a-days, to achieve an optimized solution for hard problems is a big challenge. Scientists are putting their best efforts to introduce a best algorithm to optimize the problem to a great extent. Genetic Algorithm is one of the stepping stones in the challenge and is an evolutionary algorithm inspired by Darwin's theory of evolution. Using this algorithm, with MapReduce, makes it efficient and user friendly. Users can build more scalable applications with MapReduce, since it provides a better abstraction to the genetic algorithm in lesser time. To parallelize the process of any project, MapReduce plays a vital role on Hadoop platform. The platform may vary from Hadoop to cloud which affect the performance significantly. Parallelizing of a genetic algorithm is convenient with the help of MapReduce. The major objective of the study is to know the behavior of Genetic Algorithm under the paradigm of Hadoop MapReduce. The various applications show different trends influenced by this platform. Also literature review strongly depicts the advantages of Hadoop MapReduce platform over other platforms. Moreover, the difference between various paradigms of Parallelisation is given in the paper to make decisions regarding its implementation of future work.
Year: 2015
Issue:Aug-Oct
Title:Genetic Algorithm Using MapReduce-A Critical Review
Author Name:Palak Sachar, and Vikas Khullar
Synopsis:
Now-a-days, to achieve an optimized solution for hard problems is a big challenge. Scientists are putting their best efforts to introduce a best algorithm to optimize the problem to a great extent. Genetic Algorithm is one of the stepping stones in the challenge and is an evolutionary algorithm inspired by Darwin's theory of evolution. Using this algorithm, with MapReduce, makes it efficient and user friendly. Users can build more scalable applications with MapReduce, since it provides a better abstraction to the genetic algorithm in lesser time. To parallelize the process of any project, MapReduce plays a vital role on Hadoop platform. The platform may vary from Hadoop to cloud which affect the performance significantly. Parallelizing of a genetic algorithm is convenient with the help of MapReduce. The major objective of the study is to know the behavior of Genetic Algorithm under the paradigm of Hadoop MapReduce. The various applications show different trends influenced by this platform. Also literature review strongly depicts the advantages of Hadoop MapReduce platform over other platforms. Moreover, the difference between various paradigms of Parallelisation is given in the paper to make decisions regarding its implementation of future work.
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