Parallel Random Search and Tabu Search for the Minimal Consistent Subset Selection Problem

Authors:
Vicente Cerverón;Ariadna Fuertes
Affiliations:
-;-
Venue:
RANDOM '98 Proceedings of the Second International Workshop on Randomization and Approximation Techniques in Computer Science
Year:
1998

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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 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.