Abstract
In this paper we measure the speed of several popular and recent programming languages performing the most usual operators in the canonical evolutionary algorithm, mutation and crossover, as well as an usual fitness function, OneMax. These three operations are representative of the kind of the ones performed in binary chromosomes. Our main objectives are, first, to create programs in programming languages that use the fastest available implementation. Second, to find out the differences in speeds for the different languages. Third, to find out whether the usual assumptions about the speed of languages really holds. And, finally, to find if the assumed order of speed in languages used in evolutionary algorithms holds true for all kinds of operations. In order to do that, we use available implementations or perform our own, concluding that the evolutionary algorithm scenario is more complex than usually assumed and finding out some surprising winners and losers among the languages tested.
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Notes
- 1.
Considering that Octave and Matlab actually use the same language. Besides, we have not measured proprietary implementations of languages such as that one.
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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/JJ/2016-ea-languages-PPSN. It has been supported in part by GeNeura Team http://geneura.wordpress.com, projects TIN2014-56494-C4-3-P (Spanish Ministry of Economy and Competitiveness), Conacyt Project PROINNOVA-220590.
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Merelo-Guervós, JJ. et al. (2017). Ranking Programming Languages for Evolutionary Algorithm Operations. In: Squillero, G., Sim, K. (eds) Applications of Evolutionary Computation. EvoApplications 2017. Lecture Notes in Computer Science(), vol 10199. Springer, Cham. https://doi.org/10.1007/978-3-319-55849-3_44
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