LCS Approach to Tasks Scheduling Problem in the Two Processor System

• Autorzy: Wasielewska K. , Seredyński F.
• Języki publikacji: EN

Abstrakty

EN

In this paper we propose an approach to solve multiprocessor scheduling problem with use of rule-based learning machine - Learning Classifier System (LCS). LCS combines reinforcement learning and evolutionary computing to produce adaptive systems. We interpret the multiprocessor scheduling problem as multi-step problem, where a feedback is given after some number steps. We show that LCS is able to solve scheduling tasks of a parallel program in the two processor system.

Słowa kluczowe

EN

learning classifier systems; scheduling problem; evolutionary technique

• Wydawca: Wydawnictwo Uniwersytetu Przyrodniczo-Humanistycznego w Siedlcach
• Czasopismo: Studia Informatica : systems and information technology
• Rocznik: 2007
• Tom: Vol. 2(9)
• Strony: 29--39
• Opis fizyczny: Bibliogr. 10 poz., wykr.

Twórcy

autor

Wasielewska K.

• Institute of Computer Science, Higher State School of Vocational Education, ul. Wojska Polskiego 1, 82-300 Elbląg, Poland

autor

Seredyński F.

• Institute of Computer Science, University of Podlasie, ul. Sienkiewicza 51, 08-110 Siedlce, Poland
• Institute of Computer Science, Polish Academy of Sciences, ul. Ordona 21, 01-237 Warsaw, Poland

Bibliografia

• 1. Ahmad I., Kwok Y.K, (1999). On Parallelizing the Multiprocessor Scheduling Problem, IEEE Transactions on Parallel and Distributed Systems, 10(4), 414-432.
• 2. Butz M.V., Wilson S.W., (2000). An algorithmic description of XCS, Technical Report 2000017, Illianois Genetic Algorithms Laboratory.
• 3. Holland J.H., Reitman J., (1978). Cognitive systems based on adaptive algorithms. In Waterman D., Hayess-Roth F. (Eds), Pattern-directed Inference Systems. Academic Press, New York.
• 4. Lanzi P.L., Riolo R.L., (2000). A Roadmap to the Last Decade of Learning Classifier System Research. In: Learning Classifier Systems. From Foundations to Applications, Lanzi P.L., Stolzmann W., Wilson S.W. (Eds), LNAI 1813. Springer, 33-62.
• 5. Swiecicka A., Seredynski F., Zomaya A., (2006). Multiprocessor scheduling and rescheduling with use of cellular automata and artificial immune system support, IEEE Trans. On Parallel and Distributed Systems, vol. 17, N3, 253-262.
• 6. Wasielewska K., Seredynski F., (2006). Learning Classifier Systems: a way of reinforcement learning based on evolutionary techniques. In: Evolutionary computation and global optimization, Arabas J. (Ed.), OWPW, 385-395.
• 7. Wasielewska K., Seredynski F., (2007). LCS approach to multiprocessor scheduling. In Grzech A. (Ed.), Proceedings of the 16th International Conference on Systems Science, OWPW, Wroclaw, Vol. 2,463-469.
• 8. Watkins C.J.C.H., (1989). Learning from Delayed Rewards. PhD Thesis, Cambridge University.
• 9. Wilson S.W., (1995). Classifier Fitness Based on Accuracy. Evolutionary Computation, 3(2), 149-76.
• 10. Wilson S.W., (1998). Generalization in the XCS classifier system. Proc. of the Third Annual Conference, 665-674.