Abstract
This paper reports the results of a research effort that explores time/space tradeoffs inherent to genetic algorithms (GA). The study analyzes redundancy in the GA search space and lays out a schema for efficient utilization of record keeping in the form of a cache to minimize redundancy. The application used for evaluation of the record keeping procedure is feature selection for computer workload characterization. The experimental results demonstrate the utility of record keeping in the GA domain, and show a significant reduction in execution time with virtually the same solution quality.
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Tamir, D.E., Novoa, C., Lowell, D. (2010). Time Space Tradeoffs in GA Based Feature Selection for Workload Characterization. In: García-Pedrajas, N., Herrera, F., Fyfe, C., Benítez, J.M., Ali, M. (eds) Trends in Applied Intelligent Systems. IEA/AIE 2010. Lecture Notes in Computer Science(), vol 6097. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13025-0_66
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DOI: https://doi.org/10.1007/978-3-642-13025-0_66
Publisher Name: Springer, Berlin, Heidelberg
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