Abstract
The Minimal Consistent Subset Selection (MCSS) problem is a discrete optimization problem whose resolution for large scale instances requires a prohibitive processing time. Prior algorithms addressing this problem are presented. Randomization and approximation techniques are suitable to face the problem, then random search and meta-heuristics are proposed and discussed. Specifically, Tabu Search emerges as a promising technique, consequently Tabu Search strategies are applied and evaluated. Parallel computing helps to reduce processing time and/or produce better results; different approaches for designing parallel tabu search are analyzed.
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© 1998 Springer-Verlag Berlin Heidelberg
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Cerverón, V., Fuertes, A. (1998). Parallel Random Search and Tabu Search for the Minimal Consistent Subset Selection Problem. In: Luby, M., Rolim, J.D.P., Serna, M. (eds) Randomization and Approximation Techniques in Computer Science. RANDOM 1998. Lecture Notes in Computer Science, vol 1518. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-49543-6_20
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DOI: https://doi.org/10.1007/3-540-49543-6_20
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