EXPERIMENTAL COMPARISON OF SELECTED TYPES OF PARALLEL EVOLUTIONARY ALGORITHMS

Ivan Sekaj, Marek Linder, Daniel Pernecký

2011

Abstract

Parallel evolutionary algorithms are able to improve the performance of simple evolutionary algorithms which use a single population. Their characteristics and performance depend on their architectures and other factors and parameters. In our contribution we present some viewpoints of classification and we demonstrate experimentally the influence of selected factors such as architecture type, migration topology, migration period, number of migrants, numbers of subpopulations, subpopulation size and others on the performance of these algorithms. This experimental study should help to generalise the properties and behaviour of various types of parallel evolutionary algorithms and help to design algorithms for solving hard search/optimisation problems like modelling of bio-medicine processes, optimisation of pharmaceutical dosing, optimisation of large technological and construction tasks etc.

References

  1. Alba E., Tomassini M., 2002. Parallelism and Evolutionary Algorithms. In IEEE Trans. on Evolutionary Computation, Vol. 6, NO.5.
  2. Cantú-Paz E., 1995. A Summary of Research on Parallel Genetic Algorithms. In IlliGAL Report No. 95007. University of Illinois at Urbana-Champaign.
  3. Cantú-Paz E., 1999. Migration Policies, Selection Pressure, and Parallel Evolutionary Algorithms. In IlliGAL Report 99015, University of Illinois at Urbana-Champaign.
  4. Giacobini M., Tomassini M., Tettamanzi A.G.B., Alba E., 2005. Selection intensity in cellular evolutionary algorithms for regular lattices. In IEEE Transactions on Evolutionary Computation.
  5. Lin S. Ch., Punch W., Goodman E., 1994. Coarse-grain parallel genetic algorithms: Categorization and new approach. In IEEE Symposium on Parallel and Distributed Processing.
  6. Nowostawski M., Poli R., 1999. Parallel Genetic Algorithm Taxonomy. In KES'99.
  7. Sekaj I., 2004. Robust Parallel Genetic Algorithms with Re-Initialisation. In PPSN VIII, September 18-22, Birmingham.
  8. Sekaj I., Perkacz J., 2007. Some Aspects of Parallel Genetic Algorithms with Population Re-initialization. In CEC, Singapore.
  9. Sekaj, I., Oravec, M., 2009. Selected Population Characteristics of Fine-grained Parallel Genetic Algorithms with Re-initialisation. In Proceedings of the GEC 2009, Shanghai.
  10. Skolicki Z., DeJong K., 2005. The influence of migration sizes and intervals on island models. In GECCO, Washington, USA.
  11. Whitley D., Rana S., Heckendorn R. B.,1999. The island model genetic algorithm: On separability, population size and convergence. In Journal of Computing and Information Technology, 7(1), pp.33-47.
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Paper Citation


in Harvard Style

Sekaj I., Linder M. and Pernecký D. (2011). EXPERIMENTAL COMPARISON OF SELECTED TYPES OF PARALLEL EVOLUTIONARY ALGORITHMS . In Proceedings of the International Conference on Evolutionary Computation Theory and Applications - Volume 1: ECTA, (IJCCI 2011) ISBN 978-989-8425-83-6, pages 296-302. DOI: 10.5220/0003655402960302


in Bibtex Style

@conference{ecta11,
author={Ivan Sekaj and Marek Linder and Daniel Pernecký},
title={EXPERIMENTAL COMPARISON OF SELECTED TYPES OF PARALLEL EVOLUTIONARY ALGORITHMS},
booktitle={Proceedings of the International Conference on Evolutionary Computation Theory and Applications - Volume 1: ECTA, (IJCCI 2011)},
year={2011},
pages={296-302},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003655402960302},
isbn={978-989-8425-83-6},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Evolutionary Computation Theory and Applications - Volume 1: ECTA, (IJCCI 2011)
TI - EXPERIMENTAL COMPARISON OF SELECTED TYPES OF PARALLEL EVOLUTIONARY ALGORITHMS
SN - 978-989-8425-83-6
AU - Sekaj I.
AU - Linder M.
AU - Pernecký D.
PY - 2011
SP - 296
EP - 302
DO - 10.5220/0003655402960302