An genetic algorithm approach for profiling computational performance measures
- Published
- Accepted
- Subject Areas
- Adaptive and Self-Organizing Systems, Algorithms and Analysis of Algorithms, Artificial Intelligence, Data Mining and Machine Learning, Databases
- Keywords
- Genetic Algorithm, Performance Analysis, Clustering, Profiling
- Copyright
- © 2015 Sant Ana Lima
- Licence
- This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ PrePrints) and either DOI or URL of the article must be cited.
- Cite this article
- 2015. An genetic algorithm approach for profiling computational performance measures. PeerJ PrePrints 3:e819v1 https://doi.org/10.7287/peerj.preprints.819v1
Abstract
This paper present a Genetic Algorithm(GA) approach for clustering data metric of computational performance measures collected from vmstat and sar tools. The proposed work models the genes, chromosomes, species and environment based on the dataset and presents an algorithm to analyze patterns and classify the records. The proposed method submits the performance information to an N-Dimensional Histogram in order to obtain the distribution of data that is used as input to the cluster initialization. The individual from each species undergoes successive crossover, mutation and selection operations to improve and evolve the initial population to a given environment state. The fitness-function is determined by the N-Dimensional Euclidean distance. The selection method is based on the Roulette-Wheel Selection, Elitist Selection and Truncation Selection. The results presented were obtained from seven test scenarios.
Author Comment
This is version 1 of a submission to PeerJ Computer Science peer-review journal.