In particular, little research has been published on GPGPU parallelization of genetic algorithms. Genetic algorithms are inherently sequential in nature, but there is still significant parallelization potential within each generation. This research presents a framework for GPGPU parallel processing of genetic algorithms, and demon...
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SHINDE, G. N., JAGTAP, Sudhir B., et PANI, Subhendu Kumar. Parallelizing multi-objective evolutionary genetic algorithms. In : Proceedings of the World Congress on Engineering. 2011.S.B. Jagtap, Subhendu Kumar Pani & G.N. Shinde "Parallelizing multi-objective evolutionary Genetic Algorithms" ...
Parallel genetic algorithmsThe MapReduce programming model allows users to easily develop distributed applications in data centers. However, many applications cannot be exactly expressed with MapReduce due to their specific characteristics. For instance, Genetic Algorithms (GAs) naturally fit into an ...
Parallelizing Multi-objective Evolutionary Genetic AlgorithmsGaneshchandra ShindeMember IAENGSudhir JagtapDr Subhendu Pani