Czasopismo
2009
|
Vol. 95, nr 2/3
|
263-285
Tytuł artykułu
Autorzy
Wybrane pełne teksty z tego czasopisma
Warianty tytułu
Języki publikacji
Abstrakty
The mathematical model of the biologically inspired, memetic, agent-based computation systems EMAS and iEMAS conformed to BDI standard is presented. The state of the systems and their dynamics are expressed as stationary Markov chains. Such an approach allows to better understand their complex behavior as well as their limitations.
Słowa kluczowe
Czasopismo
Rocznik
Tom
Strony
263-285
Opis fizyczny
Bibliogr. 26 poz.
Twórcy
autor
autor
- Department of Computer Science, AGH University of Science and Technology, Al. Mickiewicza 30, 30-059 Kraków, Poland, schaefer@agh.edu.pl
Bibliografia
- [1] Billingsley, P.: Probability and Measure, JohnWilley and Sons, 1987.
- [2] Byrski, A., Kisiel-Dorohinicki, M.: Immunological selection mechanism in agent-based evolutionary computation, Intelligent Information Processing and Web Mining : proceedings of the international IIS: IIPWM '05 conference : Gdansk, Poland (M. A. Klopotek, S. T.Wierzchon, K. Trojanowski, Eds.), Advances in Soft Computing, Springer Verlag, 2005.
- [3] Byrski, A., Kisiel-Dorohinicki,M.: Agent-Based Evolutionary and Immunological Optimization, Computational Science - ICCS 2007, 7th International Conference, Beijing, China, May 27 - 30, 2007, Proceedings, Springer, 2007.
- [4] Byrski, A., Kisiel-Dorohinicki,M., Nawarecki, E.: Agent-Based Evolution of Neural Network Architecture, Proc. of the IASTED Int. Symp.: Applied Informatics (M. Hamza, Ed.), IASTED/ACTA Press, 2002.
- [5] Byrski, A., Schaefer, R.: Immunological mechanism for asynchronous evolutionary computation boosting, ICMAM 2008 : European workshop on Intelligent Computational Methods and Applied Mathematics : an international forum for researches, teachers and students : Cracow, Poland, 2008.
- [6] Byrski, A., Schaefer, R.: Formal Model for Agent-Based Asynchronous Evolutionary Computation, Proceedings of IEEE Congress on Evolutionary Computation 2009 (IEEE CEC 2009), IEEE Computational Intelligence Society, IEEE Press, Trondheim, Norway, 18-21 May 2009.
- [7] Cantú-Paz, E.: A summary of research on parallel genetic algorithms, IlliGAL Report No. 95007. University of Illinois, 1995.
- [8] Centarowicz, K., Cieciwa, R., Nawarecki, E., Rojek, G.: Unfavorable Behavior Detection in Real World Systems Using the Multiagent System, Intelligent Information Processing and Web Mining, 2005.
- [9] Cetnarowicz, K., Kisiel-Dorohinicki,M., Nawarecki, E.: The application of evolution process in multi-agent world (MAW) to the prediction system, Proc. of the 2nd Int. Conf. on Multi-Agent Systems (ICMAS'96) (M. Tokoro, Ed.), AAAI Press, 1996.
- [10] Cotta, C., Fernandez, A. J.: Memetic Algorithms in Planning, Scheduling and Timetabling, Evolutionary Scheduling (P. C. K. Dahal, K.-C. Tan, Ed.), Springer Verlag, 2007.
- [11] Dasgupta, D.: Artificial Immune Systems and Their Applications, Springer-Verlag, 1998.
- [12] Jennings, N. R., Sycara, K., Wooldridge, M.: A Roadmap of Agent Research and Development, Journal of Autonomous Agents and Multi-Agent Systems, 1(1), 1998, 7-38.
- [13] Jennings, N. R., Wooldridge,M. J.: Software Agents, IEE Review, 1996, 17-20.
- [14] Kisiel-Dorohinicki, M.: Agent-Oriented Model of Simulated Evolution, SofSem 2002: Theory and Practice of Informatics (W. I. Grosky, F. Plasil, Eds.), 2540, Springer-Verlag, 2002.
- [15] Lozano, M., Martinez, G.: An evolutionary ILS-Perturbation Technique, LNCS 5296. Proceedings of the International Workshop on Hybrid Metaheuristics. HM 2008 (M. J. Blesa, et al., Eds.), Springer, 2008.
- [16] Michalewicz, Z.: Genetic Algorithms Plus Data Structures Equals Evolution Programs, Springer-Verlag New York, Inc., Secaucus, NJ, USA, 1994, ISBN 0387580905.
- [17] Mitsumoto, N., Fukuda, T., Arai, F.: The immunemechanism, adaptation, learning for the multi agent system, Emerging Technologies and Factory Automation, 6-10, 1994.
- [18] Molina, D., Lozano, M., Martinez, C. G., Herrera, F.: Memetic Algorithm for Intense local Search Methods Using Local Search Chain, LNCS 5296. Proceedings of the International Workshop on Hybrid Metaheuristics. HM 2008 (M. J. Blesa, et al., Eds.), Springer, 2008.
- [19] Montes de Oca, M. A., Van den Enden, K., Stützle, T.: Incremental Particle Swarm-Guided Local Search for Continuous Optimization, LNCS 5296. Proceedings of the International Workshop on Hybrid Metaheuristics. HM 2008 (M. J. Blesa, et al., Eds.), Springer, 2008.
- [20] Moscato, P.: Memetic algorithms: a short introduction, New ideas in optimization, McGraw-Hill Ltd., UK, Maidenhead, UK, England, 1999.
- [21] Nishiyama, H., Mizoguchi, F.: Design of Security System Based on Immune System, Tenth IEEE International Workshops on Enabling Technologies: Infrastructure for Collaborative Enterprises, 2001.
- [22] Rudolph, G.: Evolution Strategies, Handbook of Evolutionary Computations (T. B¨ack, D. Fogel, Z. Michalewicz, Eds.), Oxford University Press, 1997.
- [23] Rudolph, G.: Models of Stochastic Convergence, Handbook of Evolutionary Computations (T. B¨ack, D. Fogel, Z. Michalewicz, Eds.), Oxford University Press, 1997.
- [24] Rudolph, G.: Stochastic Processes, Handbook of Evolutionary Computations (T. B¨ack, D. Fogel, Z. Michalewicz, Eds.), Oxford University Press, 1997.
- [25] Schaefer, R., Byrski, A.: Stochastic Model of Evolutionary and Immunological Multi-Agent Systems: Parallel Execution of Local Actions, Fundamenta Informaticae, 2009.
- [26] Vose,M.: The Simple Genetic Algorithm: Foundations and Theory, MIT Press, Cambridge, MA, USA, 1998, ISBN 026222058X.
Typ dokumentu
Bibliografia
Identyfikatory
Identyfikator YADDA
bwmeta1.element.baztech-article-BUS8-0005-0080