Abstract
In this article a web tool which contributes to the visual analysis of the Differential Evolution (DE) algorithms is presented. The tool provides a graphic interface with 8 views that allows understanding the underlying process of the algorithm. The tool has a library which extracts data from DE algorithms and its main feature is that the functions of the library can be embedded in the code of any DE algorithm to be analyzed. To validate the tool, three DE algorithms: DE/Rand/1/bin, DE/best/1/bin, and JADE and three test functions: Sphere, Rosenbrock, and Rastrigin have been used, which produced a total of 234 different tests, all of them performed successfully. The tool can allow to experts to analyze algorithms, particularly DE algorithms, and it can contribute to improve such algorithms or in generating new strategies that can emerge from the analysis of the extracted information.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Hart, E., Ross, P.: GAVEL - a new tool for genetic algorithm visualization. IEEE Trans. Evol. Comput. 5(2), 335–348 (2001)
Wu, H.-C., Sun, C.-T., Lee, S.-S.: Visualization of evolutionary computation processes: from the perspective of population. In: Proceedings of the Fifth World Congress on Intelligent Control and Automation, pp. 2077–2081. IEEE Xplore, Hangzhou (2004)
Kerren, A., Egger, T.: EAVis: a visualization tool for evolutionary algorithms. In: IEEE Symposium on Visual Languages and Human-Centric Computing, pp. 299–301. IEEE Xplore, Texas (2005)
Lutton, E., Fekete, J.D.: Visual analytics and experimental analysis of evolutionary algorithms. INRIA Rapport de recherche 7605 (2011)
Bullock, S., Bedau, M.A.: Exploring the dynamics of adaptation with evolutionary activity plots. Artif. Life 12(2), 193–197 (2006)
Pohlheim, H.: Visualization of evolutionary algorithms - set of standard techniques and multidimensional visualization. In: Proceedings of the 1st Annual Conference on Genetic and Evolutionary Computation, vol. 1, pp. 533–540. Morgan Kaufmann Publishers Inc., San California (1999)
Lotif, M.: Visualizing the population of meta-heuristics during the optimization process using self-organizing maps. In: IEEE Congress on Evolutionary Computation, pp. 312–319. IEEE Xplore, Beijing (2014)
Mach, M., Zetakova, Z.: Visualising genetic algorithms: a way through the labyrinth of search space. In: Sincak, P., Vascak, J., Kvasnicak, V., Pospichal, J. (eds.) Intelligent Technologies-Theory and Applications, pp. 279–285. IOS Press, Amsterdam (2002)
The R Foundation: Comprehensive R Archive Network. The R Project for Statistical Computing (2015). http://www.r-project.org/index.html
Institut de Radioprotection et de Sûreté Nucléaire, WebGL in Shiny. http://trestletechgithub.io/shinyRGL/. Accessed 2013
Pérez, J., Alvarado, L., Almanza, N., Mexicano, A., Zavala, C.: A graphical visualization tool for analyzing the behavior of metaheuristic algorithms. In: Proceedings of ICITSEM, Dubai, UAE, pp. 120–124 (2014)
Suganthan, P.N., Hansen, N., Liang, J., Deb, K., Chen, Y.P., Auger, A., Tiwari, S.: Problem Definitions and Evaluation Criteria for the CEC 2005 Special Session on Real-Parameter Optimization. Nanyang Technological University, Singapore (2005)
Price, K.V., Storn, R.M., Lampinen, J.A.: Differential Evolution: A Practical Approach to Global Optimization. Springer, New York (2005)
Zhang, J., Sanderson, A.C.: JADE: adaptive differential evolution with optional external archive. IEEE Trans. Evol. Comput. 13(5), 945–958 (2009)
Acknowledgements
The project is supported by a research grant No. DSA/103.5/16/10473 awarded by PRODEP and by Autonomous University of Ciudad Juarez. Title - Detection of Cardiac Arrhythmia Patterns through Adaptive Analysis.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Mexicano-Santoyo, A., Rodríguez-Jorge, R., Abrego, A., Jiménez, M.A., Zúñiga-Treviño, R., Martínez-Garcia, E.A. (2019). Visual Analysis of Differential Evolution Algorithms. In: Choroś, K., Kopel, M., Kukla, E., Siemiński, A. (eds) Multimedia and Network Information Systems. MISSI 2018. Advances in Intelligent Systems and Computing, vol 833. Springer, Cham. https://doi.org/10.1007/978-3-319-98678-4_51
Download citation
DOI: https://doi.org/10.1007/978-3-319-98678-4_51
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-98677-7
Online ISBN: 978-3-319-98678-4
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)