Abstract
In this paper we present the results of a research relative to the ascertainment of limits and potentials of genetic algorithms [4, 3, 6] in addressing highly constrained optimization problems, where a minimal change to a feasible solution is very likely to yield an infeasible one. As a test problem, we have chosen the timetable problem (TTP), a problem that is known to be NP-hard [5], which has been intensively investigated for its practical relevance [2, 1]
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Chahal, N., and D. deWerra. An interactive system for constructing timetables on a PC. European Journal of Operational Research, 40 (1), 1989.
Davis, L., and F. Ritter. Schedule optimization with probabilistic search. Proc. 3rd IEEE Conference on Artificial Intelligence Applications, Orlando, Florida, February 1987.
De Jong, K. A.,and W. M. Spears. Using genetic algorithms to solve NP-complete problems. Proc. 3rd Int. Conference on Genetic Algorithms and Their Applications, George Mason University, June 1989.
M. Dorigo. Genetic algorithms:The state of the art and some research proposal. Technical Report No. 89–058, Polytecnico di Milano, Italy, 1989.
Even, S., A. Itai, and A. Shamir. On the complexity of timetable and multicommodity flow problems. SIAM Journal of Computing, 5 (4), December 1976.
D. E. Goldberg. Genetic Algorithms in Search, Optimization and Machine Learning,Addison-Wesley, 1989.
H. Mühlenbein. Parallel genetic algorithms, population genetics and combinatorial optimization. Proc. 3rd Int. Conference on Genetic Algorithms an Their Application, George Mason University, June 1989.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 1992 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Colorni, A., Dorigo, M., Maniezzo, V. (1992). Genetic Algorithms: A New Approach to the Timetable Problem. In: Akgül, M., Hamacher, H.W., Tüfekçi, S. (eds) Combinatorial Optimization. NATO ASI Series, vol 82. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-77489-8_14
Download citation
DOI: https://doi.org/10.1007/978-3-642-77489-8_14
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-77491-1
Online ISBN: 978-3-642-77489-8
eBook Packages: Springer Book Archive