Ranking the Performance of Compiled and Interpreted Languages in Genetic Algorithms

Juan Julián Merelo-Guervós, Israel Blancas-Álvarez, Pedro A. Castillo, Gustavo Romero, Pablo García-Sánchez, Víctor M. Rivas, Mario García-Valdez, Amaury Hernández-Águila, Mario Román

Abstract

Despite the existence and popularity of many new and classical computer languages, the evolu- tionary algorithm community has mostly exploited a few popular ones, avoiding them, especially if they are not compiled, under the asumption that compiled languages are always faster than interpreted languages. Wide-ranging performance analyses of implementation of evolutionary al- gorithms are usually focused on algorithmic implementation details and data structures, but these are usually limited to specific languages. In this paper we measure the execution speed of three common operations in genetic algorithms in many popular and emerging computer languages us- ing different data structures and implementation alternatives, with several objectives: create a ranking for these operations, compare relative speeds taking into account different chromosome sizes and data structures, and dispel or show evidence for several hypotheses that underlie most popular evolutionary algorithm libraries and applications. We find that there is indeed basis to consider compiled languages, such as Java, faster in a general sense, but there are other languages, including interpreted ones, that can hold its ground against them.

References

  1. Alba, E., Ferretti, E., and Molina, J. M. (2007). The influence of data implementation in the performance of evolutionary algorithms. In Computer Aided Systems Theory-EUROCAST 2007, pages 764-771. Springer.
  2. Erb, B. and Kargl, F. (2015). A conceptual model for eventsourced graph computing. In Proceedings of the 9th ACM International Conference on Distributed EventBased Systems, DEBS 7815, pages 352-355, New York, NY, USA. ACM.
  3. Fortin, F.-A., Rainville, D., Gardner, M.-A. G., Parizeau, M., Gagné, C., et al. (2012). Deap: Evolutionary algorithms made easy. The Journal of Machine Learning Research, 13(1):2171-2175.
  4. García-S ánchez, P., González, J., Castillo, P., Merelo, J., Mora, A., Laredo, J., and Arenas, M. (2010). A Distributed Service Oriented Framework for Metaheuristics Using a Public Standard. In Nature Inspired Cooperative Strategies for Optimization (NICSO 2010), pages 211-222. Springer.
  5. García-S ánchez, P., González, J., Castillo, P.-A., GarcíaArenas, M., and Merelo-Guervós, J.-J. (2013). Service oriented evolutionary algorithms. Soft Comput., 17(6):1059-1075.
  6. Jose Filho, L. R., Treleaven, P. C., and Alippi, C. (1994). Genetic-algorithm programming environments. Computer, 27(6):28-43.
  7. Merelo, J.-J., García-S ánchez, P., García-Valdez, M., and Blancas, I. (2015). There is no fast lunch: an examination of the running speed of evolutionary algorithms in several languages. ArXiv e-prints.
  8. Merelo-Guervós, J.-J., Romero, G., García-Arenas, M., Castillo, P. A., Mora, A.-M., and Jiménez-Laredo, J.-L. (2011). Implementation matters: Programming best practices for evolutionary algorithms. In Cabestany, J., Rojas, I., and Caparrós, G. J., editors, IWANN (2), volume 6692 of Lecture Notes in Computer Science, pages 333-340. Springer.
  9. Merelo-Guervós, J.-J., Castillo, P.-A., and Alba, E. (2010). Algorithm::Evolutionary, a flexible Perl module for evolutionary computation. Soft Computing, 14(10):1091-1109. Accesible at http://sl.ugr.es/000K.
  10. Namiot, D. and Sneps-Sneppe, M. (2014). On microservices architecture. International Journal of Open Information Technologies, 2(9):24-27.
  11. Nesmachnow, S., Luna, F., and Alba, E. (2015). An empirical time analysis of evolutionary algorithms as c programs. Software: Practice and Experience, 45(1):111-142.
  12. TIOBE team (2016). Tiobe index for april 2016. Technical report, TIOBE.
  13. Wolpert, D. H. and Macready, W. G. (1997). No free lunch theorems for optimization. IEEE Transactions on Evolutionary Computation, 1(1):67-82.
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Paper Citation


in Harvard Style

Merelo-Guervós J., Blancas-Álvarez I., A. Castillo P., Romero G., García-Sánchez P., M. Rivas V., García-Valdez M., Hernández-Águila A. and Román M. (2016). Ranking the Performance of Compiled and Interpreted Languages in Genetic Algorithms . In Proceedings of the 8th International Joint Conference on Computational Intelligence - Volume 1: ECTA, (IJCCI 2016) ISBN 978-989-758-201-1, pages 164-170. DOI: 10.5220/0006048101640170


in Bibtex Style

@conference{ecta16,
author={Juan Julián Merelo-Guervós and Israel Blancas-Álvarez and Pedro A. Castillo and Gustavo Romero and Pablo García-Sánchez and Víctor M. Rivas and Mario García-Valdez and Amaury Hernández-Águila and Mario Román},
title={Ranking the Performance of Compiled and Interpreted Languages in Genetic Algorithms},
booktitle={Proceedings of the 8th International Joint Conference on Computational Intelligence - Volume 1: ECTA, (IJCCI 2016)},
year={2016},
pages={164-170},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006048101640170},
isbn={978-989-758-201-1},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 8th International Joint Conference on Computational Intelligence - Volume 1: ECTA, (IJCCI 2016)
TI - Ranking the Performance of Compiled and Interpreted Languages in Genetic Algorithms
SN - 978-989-758-201-1
AU - Merelo-Guervós J.
AU - Blancas-Álvarez I.
AU - A. Castillo P.
AU - Romero G.
AU - García-Sánchez P.
AU - M. Rivas V.
AU - García-Valdez M.
AU - Hernández-Águila A.
AU - Román M.
PY - 2016
SP - 164
EP - 170
DO - 10.5220/0006048101640170