Javascript must be enabled for the correct page display

Genetic Tuning

Gelder, S. van (2002) Genetic Tuning. Master's Thesis / Essay, Artificial Intelligence.

[img]
Preview
Text
AI_Ma_2002_SvanGelder.CV.pdf - Published Version

Download (2MB) | Preview

Abstract

Bolesian (a part of Cap Gemini Ernst & Young, specialized in knowledge-based solutions) develops matching applications for the vacancy and resume domain. Those matching applications are often used by HRM-departments (Human Resource Management) of companies or by temping agencies. Those matching applications assist them in finding the right employees for a given vacancy. On the other hand, employees can match their resumes against the available job openings. A matching algorithm compares demand to supply to calculate how close the supply matches the demand. Those match scores are ordered and the company (or employee) receives a list with the highest scores that probably meet the demands of a specific vacancy (or resume). The candidate profile (or job) with the highest match score is likely to be the best suitable profile (or job) to the job (or resume). One of the most difficult aspects of matching is tuning the application. Tuning is the balancing of the parameters of the match criteria so that the match results will appear in the right order, and the good resumes (or vacancies) score higher than a certain threshold and the bad ones score lower. This order differs from user to user (in our case HRM departments and temping agencies). One user puts a lot of emphasis on working experience, while another user values skills and education more highly. The tuning of the parameters is a manual process. It can cost days or weeks to set all the parameters correctly. Given the great number of companies that use this kind of applications, manual tuning is not really attractive. It is clear that an automated tuning process can save a lot of time, so the idea was born to use machine learning techniques to learn the correct parameter setting. The goal of this project was to determine whether or not machine learning techniques can be of use in tuning those parameters automatically. If so, which machine learning algorithms are appropriate and under what conditions can they be used. Therefore, it is investigated what can be learned by an algorithm and what must be defined within the domain. I have implemented a genetic algorithm and tested it with training data of an existing project. It turned out that a genetic algorithm is appropriate to tune the parameters automatically. The test results showed that the algorithms converges towards an optimal solution that approximates the target match scores of the existing project closely.

Item Type: Thesis (Master's Thesis / Essay)
Degree programme: Artificial Intelligence
Thesis type: Master's Thesis / Essay
Language: English
Date Deposited: 15 Feb 2018 07:30
Last Modified: 15 Feb 2018 07:30
URI: http://fse.studenttheses.ub.rug.nl/id/eprint/9055

Actions (login required)

View Item View Item