Abstract
Artificial intelligence techniques are capable to handle a large amount of information collected over the web. In this paper, big data paradigm has been studied in volunteer and grid system called Comcute that is optimized by a genetic programming scheduler. This scheduler can optimize load balancing and resource cost. Genetic programming optimizer has been applied for finding the Pareto solu-tions. Finally, some results from numerical experiments have been shown.
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
Balicki, J.: Immune Systems in Multi-criterion Evolutionary Algorithm for Task Assignments in Distributed Computer System. In: Szczepaniak, P.S., Kacprzyk, J., Niewiadomski, A. (eds.) AWIC 2005. LNCS (LNAI), vol. 3528, pp. 51–56. Springer, Heidelberg (2005)
Balicki, J.: Multicriterion Genetic Programming for Trajectory Planning of Underwater Vehicle. Int. Journal of Computer Science and Network Security 6, 1–6 (2006)
Bernaschi, M., Castiglione, F., Succi, S.: A High Performance Simulator of the Immune System. Future Generation Computer System 15, 333–342 (2006)
BOINC. Open-source software for volunteer and grid computing, http://boinc.berkeley.edu/ (accessed January 25, 2014)
Cao, L., Gorodetsky, V., Mitkas, P.A.: Agent Mining: The Synergy of Agents and Data Mining. IEEE Intelligent Systems 24(3), 64–72 (2009)
Coello Coello, C.A., Van Veldhuizen, D.A., Lamont, G.B.: Evolutionary Algorithms for Solving Multi-Objective Problems. Kluwer Academic Publishers, New York (2002)
Comcute. Volunteer grid at Gdask University of Technology, http://comcute.eti.pg.gda.pl/ (accessed January 25, 2014)
Deb, K.: Multi-Objective Optimization using Evolutionary Algorithms. John Wiley & Sons, Chichester (2001)
Draszawka, K., Szymanski, J.: Thresholding strategies for large scale multi-label text classifier. In: Proceedings of Human System Interaction IEEE (2013)
Forrest, S., Perelson, A.S.: Genetic Algorithms and the Immune System. In: Schwefel, H.-P., Männer, R. (eds.) PPSN 1990. LNCS, vol. 496, pp. 320–325. Springer, Heidelberg (1991)
Jerne, N.K.: Idiotypic Networks and Other Preconceived Ideas. Immunological Revue 79, 5–25 (1984)
Khan, D.: CAKE Classifying, Associating and Knowledge DiscovEry - An Approach for Distributed Data Mining (DDM) Using PArallel Data Mining Agents (PADMAs). In: IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology, vol. 3, pp. 596–601 (2008)
Kim, J., Bentley, P.J.: Immune Memory in the Dynamic Clonal Selection Algorithm. In: Proc. the First Int. Conference on Artificial Immune Systems, Canterbury, Australia, pp. 57–65 (2002)
Koza, J.R.: Genetic Programming: On the Programming of Computers by Means of Natural Selection. The MIT Press, Cambridge (1992)
Koza, J.R., Keane, M.A., Streeter, M.J., Mydlowec, W., Yu, J., Lanza, G.: Genetic programming IV. Routine Human-Competitive Machine Intelligence. Kluwer Academic Publishers, New York (2003)
Li, H.X., Chosler, R.: Application of Multilayered Multi-Agent Data Mining Architecture to Bank Domain. In: International Conference on Wireless Communications, Networking and Mobile Computing, WiCom 2007, pp. 6721–6724 (2007)
O’Leary, D.E.: Artificial Intelligence and Big Data. IEEE Intelligent Systems 28(2), 96–99 (2013)
Samuel, A.L.: Programming Computers to Play Games. Advances in Computers 1, 165–192 (1960)
Sheble, G.B., Britting, K.: Refined Genetic Algorithm Economic Dispatch Example. IEEE Transactions on Power Systems 10, 117–124 (1995)
Snijders, C., Matzat, U., Reips, U.D.: Big Data: Big gaps of knowledge in the field of Internet. International Journal of Internet Science 7, 1–5 (2012)
Weglarz, J., Nabrzyski, J., Schopf, J.: Grid Resource Management: State of the Art and Future Trends. Kluwer Academic Publishers, Boston (2003)
Wierzchon, S.T.: Immune-based Recommender System. In: Hryniewicz, O., Kacprzyk, J., Koronacki, J., Wierzchon, S.T. (eds.) Issues in Intelligent Systems. Paradigms, pp. 341–356. Exit, Warsaw (2005)
Zhou, D., Rao, W., Lv, F.A.: Multi-Agent Distributed Data Mining Model Based on Algorithm Analysis and Task Prediction. In: 2nd International Conference on Information Engineering and Computer Science, pp. 1–4 (2010)
Zitzler, E., Deb, K., Thiele, L.: Comparison of Multiobjective Evolutionary Algorithms: Empirical Results. Evolutionary Computation 8, 173–195 (2000)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Balicki, J., Korłub, W., Szymanski, J., Zakidalski, M. (2014). Big Data Paradigm Developed in Volunteer Grid System with Genetic Programming Scheduler. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds) Artificial Intelligence and Soft Computing. ICAISC 2014. Lecture Notes in Computer Science(), vol 8467. Springer, Cham. https://doi.org/10.1007/978-3-319-07173-2_66
Download citation
DOI: https://doi.org/10.1007/978-3-319-07173-2_66
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-07172-5
Online ISBN: 978-3-319-07173-2
eBook Packages: Computer ScienceComputer Science (R0)