Abstract
We apply the idea of learning with delayed rewards to improve performance of the Ant System. We will mention different mechanisms of delayed rewards in the Ant Algorithm (AA). The AA for JSP was first applied in classical form by A. Colorni and M. Dorigo. We adapt an idea of an evolution of the algorithm itself using the methods of the learning process. We accentuate the co-operation and stigmergy effect in this algorithm. We propose the optimal values of the parameters used in this version of the AA, derived as a result of our experiments.
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
Boryczka M., Boryczka U.: Generative policies in ant system. In: Proceedings of the EUFIT’97 Conference, Aachen, September, 1997, 857–861.
Colorni A., Dorigo M., Maniezzo U.: An Investigation of same Properties of an Ant Algorithm. In: Proceedings of the Parallel Problem Solving from Nature Conference (PPSN 92), Brussels, Belgium, Elsevier Publishing, 1992.
Colorni A., Dorigo M., Maniezzo U., Trubian M.: Ant system for Job-shop Scheduling, Belgian Journal of Operations Research, Statistic and Computer Science, 1994.
Dorigo M., Bersini H.: A comparison of Q-learning and classifier systems. In: Proceedings of From Animats to Animals Third International Conference on Simulation of Adaptive Behavior (SAB 94), Brighton UK, August 8–12, 1994.
Gambarella L.M., Dorigo M.: AntQ: A Reinforcement Learning approach to the travelling salesman problem. Proceedings of ML-95, Twelfth International Conference On Machine Learning, Morgan Kaufmann Publishers, 1995, 252–260.
Graham R.L., Lawler E.L., Lenstra J.K., Rinnooy Kan A.H.G.: Optimization and approximation in deterministic sequencing and scheduling: a survey. Annals of Discrete Mathematics, 5 (1979), 287–326.
Michalewicz, Z.: Genetic Algorithms + Data Structures = Evolution Programs. Berlin: Springer Verlag, 1996.
Singh S., Norving P., Cohn D.: Agents and Reinforcement Learning. Dr. Dobb’s Journal, March, 1997.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 1998 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Boryczka, U. (1998). Learning with Delayed Rewards in Ant Systems for the Job-Shop Scheduling Problem. In: Polkowski, L., Skowron, A. (eds) Rough Sets and Current Trends in Computing. RSCTC 1998. Lecture Notes in Computer Science(), vol 1424. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-69115-4_37
Download citation
DOI: https://doi.org/10.1007/3-540-69115-4_37
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-64655-6
Online ISBN: 978-3-540-69115-0
eBook Packages: Springer Book Archive