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Improving recruitment effectiveness using genetic programming techniques

Published: 06 July 2013 Publication History

Abstract

A real-world problem, namely to improve the recruitment effectiveness of a certain company, is tackled here by evolving accurate and human-readable classifiers by means of grammar-based genetic programming techniques.

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  • (2013)Predicting the Performance of Job Applicants by Means of Genetic ProgrammingProceedings of the 2013 BRICS Congress on Computational Intelligence and 11th Brazilian Congress on Computational Intelligence10.1109/BRICS-CCI-CBIC.2013.27(98-103)Online publication date: 8-Sep-2013

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cover image ACM Conferences
GECCO '13 Companion: Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
July 2013
1798 pages
ISBN:9781450319645
DOI:10.1145/2464576
  • Editor:
  • Christian Blum,
  • General Chair:
  • Enrique Alba
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 06 July 2013

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Author Tags

  1. competition
  2. data classification
  3. distributed genetic programming
  4. formal grammar
  5. machine learning
  6. real-world

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Conference

GECCO '13
Sponsor:
GECCO '13: Genetic and Evolutionary Computation Conference
July 6 - 10, 2013
Amsterdam, The Netherlands

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Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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  • (2013)Predicting the Performance of Job Applicants by Means of Genetic ProgrammingProceedings of the 2013 BRICS Congress on Computational Intelligence and 11th Brazilian Congress on Computational Intelligence10.1109/BRICS-CCI-CBIC.2013.27(98-103)Online publication date: 8-Sep-2013

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