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Authors: Juan Julián Merelo-Guervós 1 ; Israel Blancas-Álvarez 1 ; Pedro A. Castillo 1 ; Gustavo Romero 1 ; Pablo García-Sánchez 1 ; Víctor M. Rivas 2 ; Mario García-Valdez 3 ; Amaury Hernández-Águila 3 and Mario Román 1

Affiliations: 1 University of Granada, Spain ; 2 University of Jaén, Spain ; 3 Tijuana Institute of Technology, Mexico

ISBN: 978-989-758-201-1

Keyword(s): Benchmarking, Implementation of Evolutionary Algorithms, OneMax, Genetic Operators, Programming Languages, Performance Measurements.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Computational Intelligence ; Evolutionary Computing ; Genetic Algorithms ; Informatics in Control, Automation and Robotics ; Intelligent Control Systems and Optimization ; Representation Techniques ; Soft Computing

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 a pplications. 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. (More)

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Paper citation in several formats:
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

@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},
}

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

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