A New Method For Personnel Selection Based On Ranking Aggregation Using A Reinforcement Learning Approach

Authors

  • Yaima Filiberto Cabrera Universidad de Camaguey
  • Rafael Bello Pérez Universidad Central de Las Villas
  • Ann Nowe Vrije Universiteit Brussel

DOI:

https://doi.org/10.13053/cys-22-2-2353

Keywords:

aggregating rankings, personnel selection, reinforcement learning

Abstract

In this paper propose a new approach to the problem of aggregating rankings for obtaining an overall ranking. This is also referred to as the aggregation ranking in the personnel selection problem. Our approach is based on a distance measure between the individual and the overall ranking, and looks for the solution that minimizes the disagreement between the input rankings and the resulting aggregation. The method uses a reinforcement learning approach to build the aggregation and its performance and comparison with other approaches shows promising results.

Author Biographies

Yaima Filiberto Cabrera, Universidad de Camaguey

Director of Science and Technology

Rafael Bello Pérez, Universidad Central de Las Villas

Director of the Center of Studies on Informatics

Ann Nowe, Vrije Universiteit Brussel

Computer Science Department of the faculty of Sciences and Computer Science group of the Engineering Faculty

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Published

2018-06-29

Issue

Vol. 22 No. 2 (2018): Thematic Issue on Numerical and Evolutionary Optimization (Guest editors: L. Trujillo, Y. Maldonado, O. Schuetze)

Section

Articles

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