A Multi-Objective Genetic Algorithm for Software Personnel Staffing for HCIM Solutions

A Multi-Objective Genetic Algorithm for Software Personnel Staffing for HCIM Solutions

Enrique Jiménez-Domingo (Computer Science Department, Universidad Carlos III de Madrid, Madrid, Spain), Ricardo Colomo-Palacios (Faculty of Computer Sciences, Østfold University College, Halden, Norway) and Juan Miguel Gómez-Berbís (Computer Science Department, Universidad Carlos III de Madrid, Madrid, Spain)
Copyright: © 2014 |Pages: 16
DOI: 10.4018/ijwp.2014040103
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Abstract

The pervasive potential of artificial intelligence techniques in business scenarios has gained momentum recently through the combination of traditional software engineering disciplines and cutting-edge computer science research areas such as neural networks or genetic algorithms. Following this approach, MORGANA is a platform to perform competence oriented personnel staffing in software projects by means of a multi-objective genetic algorithm. The system is designed to be part of global human and intellectual capital management solutions. The main goal of MORGANA is to assist software project managers, by providing a comprehensible artificial intelligence-based formal framework to optimize efficiency and improve person-role fit.
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2. Background

Developing effective selection approaches represents a pivotal task for managers and companies alike (Chien & Chen, 2008). As a result of this pressure, the application of decision support systems on personnel selection and recruitment has been increasing in the last years. Thus, expert systems are shown to have matured to the point of offering real benefits in many of their applications including, among other aspects, personnel issues (Garcia-Crespo et al., 2009). In this scenario, GAs have been pointed out as accurate solutions for project management issues (Alba & Francisco Chicano, 2007).

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