Predicting the Performance of Job Applicants by Means of Genetic Programming

Author

Augusto, Douglas A. ; Bernardino, Heder S. ; Barbosa, Helio J. C.

Author_Institution

LNCC, Petropolis, Brazil

fYear

2013

fDate

8-11 Sept. 2013

Firstpage

98

Lastpage

103

Abstract

Since their early development, genetic programming-based algorithms have been showing to be successful at challenging problems, attaining several human-competitive results and other awards. This paper will present another achievement of such algorithms by describing how our team has won an international machine-learning competition. We have solved, by means of grammar-based genetic programming techniques, a real-world problem of meritocracy in jobs by evolving classifiers that were both accurate and human-readable.

Keywords

genetic algorithms; grammars; learning (artificial intelligence); recruitment; genetic programming-based algorithm; grammar-based genetic programming techniques; international machine-learning competition; job applicants; meritocracy; Accuracy; Companies; Complexity theory; Educational institutions; Genetic programming; Grammar; Real-world; competition; data classification; distributed genetic programming; formal grammar; machine learning;

fLanguage

English

Publisher

ieee

Conference_Titel

Computational Intelligence and 11th Brazilian Congress on Computational Intelligence (BRICS-CCI & CBIC), 2013 BRICS Congress on

Conference_Location

Ipojuca

Type

conf

DOI

10.1109/BRICS-CCI-CBIC.2013.27

Filename

6855836