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Benchmarking Languages for Evolutionary Algorithms

  • J. J. MereloEmail author
  • Pedro Castillo
  • Israel Blancas
  • Gustavo Romero
  • Pablo García-Sanchez
  • Antonio Fernández-Ares
  • Víctor Rivas
  • Mario García-Valdez
Conference paper
First Online:
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9598)

Abstract

Although performance is important, several other issues should be taken into account when choosing a particular language for implementing an evolutionary algorithm, such as the fact that the speed of different languages when carrying out an operation will depend on several factors, including the size of the operands, the version of the language and underlying factors such as the operating system. However, it is usual to rely on compiled languages, namely Java or C/C++, for carrying out any implementation without considering other languages or rejecting them outright on the basis of performance. Since there are a myriad of languages nowadays, it is interesting however to measure their speed when performing operations that are usual in evolutionary algorithms. That is why in this paper we have chosen three evolutionary algorithm operations: bitflip mutation, crossover and the fitness function OneMax evaluation, and measured the speed for several popular, and some not so popular, languages. Our measures confirm that, in fact, Java, C and C++ not only are the fastest, but also have a behaviour that is independent of the size of the chromosome. However, we have found other compiled language such as Go or interpreted languages such as Python to be fast enough for most purposes. Besides, these experiments show which of these measures are, in fact, the best for choosing an implementation language based on its performance.

Keywords

Evolutionary Algorithm Fitness Function Java Virtual Machine Open Source Implementation Popular Language 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Notes

Acknowledgements

This paper is part of the open science effort at the university of Granada. It has been written using knitr, and its source as well as the data used to create it can be downloaded from the GitHub repository https://github.com/geneura-papers/2015-ea-languages. It has been supported in part by GeNeura Team.

This work has been supported in part by projects TIN2014-56494-C4-3-P (Spanish Ministry of Economy and Competitiveness), SPIP2014-01437 (Dirección General de Tráfico), PRY142/14 (Fundación Pública Andaluza Centro de Estudios Andaluces en la IX Convocatoria de Proyectos de Investigación), and project V17-2015 of the Microprojects program 2015 from CEI BioTIC Granada.

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • J. J. Merelo
    • 1
    Email author
  • Pedro Castillo
    • 1
  • Israel Blancas
    • 1
  • Gustavo Romero
    • 1
  • Pablo García-Sanchez
    • 1
  • Antonio Fernández-Ares
    • 1
  • Víctor Rivas
    • 2
  • Mario García-Valdez
    • 3
  1. 1.Department of Computer Architecture and Technlogy and CITICUniversity of GranadaGranadaSpain
  2. 2.Department of InformaticsUniversity of JaénJaénSpain
  3. 3.Instituto Politécnico de TijuanaTijuanaMexico

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