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- W2471007926 abstract "Evolutionary computation (EC) is a method that is ubiquitously used to solve complex computation. Examples of EC such as Genetic Algorithm (GA) and PSO (ParticleSwarm Optimization) are prevalent due to their efficiency and effectiveness. Despite these advantages, EC suffers from long execution time due to its parallel nature.Therefore, this research explores the prospect of speeding up the EC algorithms specifically GA and PSO via MapReduce (MR) parallel processing framework. MR is an emerging parallel processing framework that hides the complex parallelization processes by employing the functional abstraction of map and reduce The Performance of the parallelized GA via MR and PSO via MR are evaluated using an analogous case study to find out the speedup and efficiency in order to measure the scalability of both proposed algorithms. Comparisons between GA via MR and PSO via MR are also established in order to find which EC algorithm scales better via MRparallel processing framework. From the results and analysis obtained from this research, it is established that both GA and PSO can be efficiently parallelized andshows good scalability via MR parallel processing framework. The Performance comparison between GA via MR and PSO via MR also shows that both algorithms arecomparable in terms of speedup and efficiency" @default.
- W2471007926 created "2016-07-22" @default.
- W2471007926 creator A5077700564 @default.
- W2471007926 date "2014-01-01" @default.
- W2471007926 modified "2023-09-24" @default.
- W2471007926 title "Analysis of evolutionary computing performance via mapreduce parallel processing architecture / Ahmad Firdaus AhmadFadzil" @default.
- W2471007926 hasPublicationYear "2014" @default.
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