Un marco de trabajo para las medidas de rendimiento en la optimización dinámica evolutiva
DOI:
https://doi.org/10.33936/isrtic.v1i1.183Abstract
Varios fenómenos reales pueden ser modelados como problemas dinámicos de optimización. Estos problemas han sido tratados eficientemente mediante métodos evolutivos durante los últimos 25 años. En este contexto, la evaluación del rendimiento de estos métodos es aún un tema en desarrollo. Sin embargo, a partir de una revisión de la literatura desarrollada en este trabajo, es posible advertir la ausencia de un marco de trabajo que se organiza convenientemente los progresos alcanzados en este campo de investigación. En consecuencia, la presente investigación tiene como objetivo proponer un marco de trabajo que permita no solo organizar los avances actuales, sino también identificar posibles medidas aún no propuestas. Se incluye además un análisis de las principales tendencias en este campo de investigación. El principal resultado obtenido a partir del marco de trabajo propuesto es el predominio de medidas de rendimiento basadas en el promedio de la calidad de la solucion obtenida por el algoritmo en términos de la funcion objetivo.
Keywords: Medidas de rendimiento, Optimización dinámica evolutiva, Evaluación de algoritmos.
Downloads
References
URL http://doi.org/http://dx.doi.org/10.1016/j.swevo.2012.05.001
[2] T. Nguyen, S. Yang, J. Branke, X. Yao, Evolutionary Dynamic Optimization: Methodologies”, publisher = Evolutionary Computation for Dynamic Optimization Problems, editor = En S. Yang and X. Yao, address = Berlin, Heidelberg, publisher = Springer Berlin Heidelberg, pages = 39–64, year = 2013, url = http://doi.org/10.1007/978-3-642-38416-5 2.
[3] J. Verdegay, R. Yager, P. Bonissone, (2008), On heuristics as a fundamental constituent of soft computing. Fuzzy Sets and Systems 159 846–855.
[4] B. Melián, M. Pérez, M. V. JA., JM., (2003), Metaheurísticas: Una vision global. Revista Iberoa- mericana de Inteligencia Artificial.
[5] I. Boussäid, J. Lepagnot, P. Siarry, A survey on optimization metaheuristics, Information Sciences 237 (2013) 82–117.
[6] P. Novoa-Hernández, D. Pelta, C. Corona, (2010), Studies in Computational Intelligence 284 371–383.
[7] C. Cruz, J. González, D. Pelta, (2011), Optimization in dynamic environments: a survey on problems, methods and measures. Soft Computing.
[8] M. Plessis, A. Engelbrecht, (2013), Metaheuristics for Dynamic Optimization 433 117–145. URL http://doi.org/10.1007/978-3-642-30665-5_7
[9] P. Novoa-Hernández, C. Corona, P. C., DA., (2016), Self-adaptation in dynamic environments - a survey and open issues. International Journal of Bio-inspired Computation.
[10] K. Weicker, Performance measures for dynamic environments, Parallel Problem Solving from Nature - PPSN VII 2439 (2002) 64–73.
URL http://doi.org/10.1007/3-540-45712-7_7
[11] J. Branke, Evolutionary optimization in dynamic environments, Genetic Algorithms and Evolutionary Computation (Vol.
[12] K. Weicker, Evolutionary Algorithms and Dynamic Optimization Problems, Institut für Formale Methoden der Informatik der Universitat Stuttgart, 2003.
[13] W. Feng, T. Brune, L. Chan, M. Chowdhury, C. Kuek, Y. Li, Benchmarks for testing evolutionary algorithms (Tech, Rep. No. CSC-97006), 1997.
[14] P. Novoa-Hernández, C. Corona, D. Pelta, (2011), Efficient multiswarm PSO algorithms for dynamic environments. Memetic Computing.
[15] P. Novoa-Hernández, C. Corona, D. Pelta, (2013), Selfadaptive, multipopulation differential evolution in dynamic environments. Soft Computing.
URL http://doi.org/10.1007/s00500-013-1022-x
[16] P. Novoa-Hernández, C. Corona, D. Pelta, (2015), A software tool for assisting experimentation in dynamic environments. Applied Computational Intelligence and Soft Computing 5.
[17] S. R., P. Eggenberger, Adaptation on the evolutionary time scale: A working hypothesis and basic experiments, Artificial Evolution: Third European Conf., AE’97, Berlin, 1997.
[18] N. Mori, H. Kita, Y. Nishikawa, Adaptation to a changing environment by means of the thermody-namical genetic algorithm, Parallel Problem Solving from Nature, 1996.
[19] J. Branke, Memory enhanced evolutionary algorithms for changing optimization problems, Proceedings of the Congress on Evolutionary Computation (Vol) 3 (1999) 1875–1882.
[20] D. Jong, K., An analysis of the behavior of a class of genetic adaptive systems, University of Michigan, 1975.
[21] K. Trojanowski, Z. Michalewicz, Searching for optima in non-stationary environments, En Evolu- tionary Computation 99. (1999) 2348.
URL http://doi.org/10.1109/CEC.1999.785498
[22] R. Morrison, Performance Measurement in Dynamic Environments, GECCO Workshop on Evolu- tionary Algorithms for Dynamic Optimization Problems, 2003.
[23] S. Bird, X. Li, Informative performance metrics for dynamic optimization problems, En 9th Conf,2007.
[24] E. Alba, B. Sarasola, Measuring fitness degradation in dynamic optimization problems, En EvoAp- plications 1 (2010) 572–581.
[25] D. Shilane, J. Martikainen, S. Ovaska, (2009), ICANNGA 2009.
[26] C. Li, S. Yang, T. Nguyen, E. Yu, X. Yao, Y. Jin, H. G. Beyer, Suganthan, PN, 2008.
Downloads
Published
How to Cite
Issue
Section
License
Articles submitted to this journal for publication will be released for open access under a Creative Commons Attribution Non-Commercial No Derivative Works licence (http://creativecommons.org/licenses/by-nc-nd/4.0).
The authors retain copyright, and are therefore free to share, copy, distribute, perform and publicly communicate the work under the following conditions: Acknowledge credit for the work specified by the author and indicate if changes were made (you may do so in any reasonable way, but not in a way that suggests that the author endorses your use of his or her work. Do not use the work for commercial purposes. In case of remixing, transformation or development, the modified material may not be distributed.
