Genetic Programming for Interaction Efficient Supporting in Volunteer Computing Systems

Part of the Studies in Computational Intelligence book series (SCI, volume 559)


Volunteer computing systems provide a middleware for interaction between project owners and great number volunteers. In this chapter, a genetic programming paradigm has been proposed to a multi-objective scheduler design for efficient using some resources of volunteer computers via the web. In a studied problem, genetic scheduler can optimize both a workload of a bottleneck computer and cost of system. Genetic programming has been applied for finding the Pareto solutions by applying an immunological procedure. Finally, some numerical experiment outcomes have been discussed.


  1. Balicki J (2005) Immune systems in multi-criterion evolutionary algorithm for task assignments in distributed computer system. Lect Notes Comput Sci 3528:51–56CrossRefGoogle Scholar
  2. Balicki J (2006) Multicriterion genetic programming for trajectory planning of underwater vehicle. J Comput Sci Netw Secur 6:1–6Google Scholar
  3. Bernaschi M, Castiglione F, Succi S (2006) A high performance simulator of the immune system. Future Gener Comput Syst 15:333–342CrossRefGoogle Scholar
  4. BOINC. Open-source software for volunteer and grid computing. Accessed 25 Oct 2013
  5. Coello CAC, Van Veldhuizen DA, Lamont GB (2002) Evolutionary algorithms for solving multi-objective problems. Kluwer Academic Publishers, New YorkCrossRefMATHGoogle Scholar
  6. Deb K (2001) Multi-objective optimization using evolutionary algorithms. Wiley, ChichesterMATHGoogle Scholar
  7. Forrest S, Perelson AS (1991) Genetic algorithms and the immune system. Lect Notes Comput Sci 496:319–325CrossRefGoogle Scholar
  8. Jerne NK (1984) Idiotypic networks and other preconceived ideas. Immunol Revue 79:5–25CrossRefGoogle Scholar
  9. Kim J, Bentley PJ (2002) Immune memory in the dynamic clonal selection algorithm. In: Proceedings of 1st international conference on artificial immune systems, Canterbury, Australia, pp 57–65Google Scholar
  10. Koza JR (1992) Genetic programming: on the programming of computers by means of natural selection. MIT Press, CambridgeMATHGoogle Scholar
  11. Koza JR, Keane MA, Streeter MJ, Mydlowec W, Yu J, Lanza G (2003) Genetic programming IV. Routine human-competitive machine intelligence. Kluwer Academic Publishers, New YorkMATHGoogle Scholar
  12. Samuel AL (1960) Programming computers to play games. Adv Comput 1:165–192CrossRefMathSciNetGoogle Scholar
  13. Sheble GB, Britting K (1995) Refined genetic algorithm—economic dispatch example. IEEE Trans Power Syst 10:117–124CrossRefGoogle Scholar
  14. Weglarz J, Nabrzyski J, Schopf J (2003) Grid resource management: state of the art and future trends. Kluwer Academic Publishers, BostonGoogle Scholar
  15. Wierzchon ST (2005) Immune-based recommender system. In: Hryniewicz O, Kacprzyk J, Koronacki J, Wierzchon ST (eds) Issues in intelligent systems. Paradigms. Exit, Warsaw, pp 341–356Google Scholar
  16. Zitzler E, Deb K, Thiele L (2000) Comparison of multiobjective evolutionary algorithms: empirical results. Evol Comput 8:173–195CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • J. Balicki
    • 1
  • W. Korłub
    • 1
  • H. Krawczyk
    • 1
  • J. Paluszak
    • 1
  1. 1.Faculty of Telecommunications, Electronics and InformaticsGdansk University of TechnologyGdańskPoland

Personalised recommendations