Asynchronous and implicitly parallel evolutionary computation models

Abstract

This paper presents the design and the application of asynchronous models of parallel evolutionary algorithms. An overview of the existing parallel evolutionary algorithm (PEA) models and available implementations is given. We present new PEA models in the form of asynchronous algorithms and implicit parallelization, as well as experimental data on their efficiency. The paper also discusses the definition of speedup in PEAs and proposes an appropriate speedup measurement procedure. The described parallel EA algorithms are tested on problems with varying degrees of computational complexity. The results show good efficiency of asynchronous and implicit models compared to existing parallel algorithms.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

References

  1. Acampora G, Gaeta M, Loia V (2011) Combining multi-agent paradigm and memetic computing for personalized and adaptive learning experiences. Comput Intell 27(2):141–165

    Article  MathSciNet  Google Scholar 

  2. Acampora G, Gaeta M, Loia V (2011) Hierarchical optimization of personalized experiences for e-learning systems through evolutionary models. Neural Comput. Appl. 20(5):641–657. doi:10.1007/s00521-009-0273-z

    Article  Google Scholar 

  3. Alba E (2002) Parallel evolutionary algorithms can achieve super-linear performance. Inf Process Lett 82:7–13

    Article  MATH  MathSciNet  Google Scholar 

  4. Alba E, Luna F, Nebro AJ (2004) Parallel heterogeneous genetic algorithms for continuous optimization. Parallel Comput 14:2004

    MathSciNet  Google Scholar 

  5. Alba E, Nebro AJ, Troya JM (2002) Heterogeneous computing and parallel genetic algorithms. J Parallel Distrib Comput 62(9):1362–1385

    Article  MATH  Google Scholar 

  6. Alba E, Tomassini M (2002) Parallelism and evolutionary algorithms. IEEE Trans Evol Comput 6:443–462

    Article  Google Scholar 

  7. Alba E, Troya JM (2001) Analyzing synchronous and asynchronous parallel distributed genetic algorithms. Future Gener Comput Syst 17(4):451–465

    Article  MATH  Google Scholar 

  8. Borovska, P (2006).: Solving the travelling salesman problem in parallel by genetic algorithm on multicomputer cluster. In: international conference on computer systems and technologies – CompSysTech06, pp. 11–1-11-6.

  9. Cahon, S., Melab, N., Talbi, E.G (2004) Building with paradiseo reusable parallel and distributed evolutionary algorithms. Parallel Computing 30(5–6), 677–697. Parallel and nature-inspired computational paradigms and applications.

    Google Scholar 

  10. Cantú-Paz, E (1998) Designing efficient master-slave parallel genetic algorithms. In: genetic programming 1998: proceedings of the third annual conference, Morgan Kaufmann, University of Wisconsin, USA, pp 455.

  11. Cantú-Paz, E (2007) Parameter setting in parallel genetic algorithms. In: Parameter setting in evolutionary algorithms, pp. 259–276.

  12. Caraffini F, Neri F, Iacca G, Mol A (2013) Parallel memetic structures. Inf Sci 227:60–82

    Article  MathSciNet  Google Scholar 

  13. Eklund, S.E (2004) A massively parallel architecture for distributed genetic algorithms. Parallel computing 30(5–6), 647–676. (Parallel and nature-inspired computational paradigms and applications).

    Google Scholar 

  14. Gagne, C., Parizeau, M., Dubreuil, M (2003) Distributed beagle: an environment for parallel and distributed evolutionary computations. In: proceedings 17th annual international symposium of high performance computing systems and applications (HPCS).

  15. Golub, M (2001) Improving the efficiency of parallel genetic algorithms, Ph.D. thesis, Faculty of Electrical Engineering and Computing, Zagreb, Croatia.

  16. Golub, M., Budin, L (2000) An asynchronous model of global parallel genetic algorithms. In: C. Fyfe (ed.) Proceedings of 2nd ICSC Symposium on Engineering of Intertnational Systems, EIS2000, pp. 353–359. ICSC Academic Press, UK.

  17. Golub, M., Jakobovic, D., Budin, L (2001) Parallelization of elimination tournament selection without synchronization. In: proceedings of the 5th IEEE international conference on intelligent engineering systems INES 2001, pp. 85–89. Institute of Production Engineering, Helsinki, Finland.

  18. Golub, M., Posavec, A.B (1997) Using genetic algorithms for adapting approximation functions. In: proceedings of the international conference ITI ’97, University Computing Centre, University of Zagreb, Pula, pp. 451–456.

  19. He H, Skora O, Salagean A, Mkinen E (2007) Parallelisation of genetic algorithms for the 2-page crossing number problem. J Parallel Distrib Comput 67(2):229–241

    Article  MATH  Google Scholar 

  20. Jakobovic D, Budin L (2006) Dynamic scheduling with genetic programming. Lect Notes Comput Sci 3905:73

    Article  Google Scholar 

  21. Jakobovic D, Jelenković L, Budin L (2007) Genetic programming heuristics for multiple machine scheduling. Lect Notes Comput Sci 4445:321–330

    Article  Google Scholar 

  22. Jakobovic D, Marasovic K (2012) Evolving priority scheduling heuristics with genetic programming. Appl Soft Comput 12(9):2781–2789. doi:10.1016/j.asoc.2012.03.065

    Article  Google Scholar 

  23. Melab, N., Cahon, S., Talbi, E.G (2006) Grid computing for parallel bioinspired algorithms. J Parallel Distrib Comput 66(8), 1052–1061 . (Parallel Bioinspired Algorithms)

    Google Scholar 

  24. Morton, T.E., Pentico, D.W (1993) Heuristic Scheduling Systems. Wiley Inc., USA.

  25. Nowostawski, M., Poli, R (1999) Dynamic demes parallel genetic algorithm. In: KES’99, proceedings of the international conference, IEEE, pp. 93–98.

  26. Nowostawski, M., Poli, R (1999) Parallel genetic algorithm taxonomy. In: Proceedings of the third International Conference knowledge-based intell information engng systems KES’99, pp. 88–92. IEEE.

  27. Park HH, Grings A, dos Santos MV, Soares AS (2008) Parallel hybrid evolutionary computation: automatic tuning of parameters for parallel gene expression programming. Appl Math Comput 201(1–2):108–120

    Article  MATH  MathSciNet  Google Scholar 

  28. Schneburg, E., Heinzmann, F., Feddersen, S (1995) Genetische Algorithmen und Evolutionsstrategien. 978–3893194933. Addison-Wesley, Verlag.

  29. Sullivan, K., Luke, S., Larock, C., Cier, S., Armentrout, S (2008) Opportunistic evolution: efficient evolutionary computation on large-scale computational grids. In: proceedings of the 2008 conference on genetic and evolutionary computation, GECCO ’08, ACM, New York, pp 2227–2232 .

  30. Weber M, Neri F, Tirronen V (2011) Shuffle or update parallel differential evolution for large-scale optimization. Soft Comput 15(11):2089–2107

    Article  Google Scholar 

Download references

Author information

Affiliations

Authors

Corresponding author

Correspondence to Domagoj Jakobović.

Additional information

Communicated by G. Acampora.

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Jakobović, D., Golub, M. & Čupić, M. Asynchronous and implicitly parallel evolutionary computation models. Soft Comput 18, 1225–1236 (2014). https://doi.org/10.1007/s00500-013-1140-5

Download citation

Keywords

  • Evolutionary algorithms
  • Parallelization
  • Asynchronous algorithms