Recent Trends in the Use of Statistical Tests for Comparing Swarm and Evolutionary Computing Algorithms: Practical Guidelines and a Critical Review
Identificadores
URI: http://hdl.handle.net/10481/68545Metadatos
Mostrar el registro completo del ítemAutor
Carrasco, Jacinto; García López, Salvador; Rueda García, María Del Mar; Das, Swagatam; Herrera Triguero, FranciscoMateria
statistical tests optimisation parametric non-parametric Bayesian
Fecha
2020-05Referencia bibliográfica
J. Carrasco, S. García, M.M. Rueda, S. Das, F. Herrera, Recent trends in the use of statistical tests for comparing swarm and evolutionary computing algorithms: Practical guidelines and a critical review, Swarm and Evolutionary Computation, Volume 54, 2020, 100665, ISSN 2210-6502, https://doi.org/10.1016/j.swevo.2020.100665.
Patrocinador
Spanish Ministry of Economy, Industry and Competitiveness; Spanish Ministry of ScienceResumen
A key aspect of the design of evolutionary and swarm intelligence algorithms is studying their performance. Statistical comparisons are also a crucial part which allows for reliable conclusions to be drawn. In the present paper we gather and examine the approaches taken from different perspectives to summarise the assumptions made by these statistical tests, the conclusions reached and the steps followed to perform them correctly. In this paper, we conduct a survey on the current trends of the proposals of statistical analyses for the comparison of algorithms of computational intelligence and include a description of the statistical background of these tests. We illustrate the use of the most common tests in the context of the Competition on single-objective real parameter optimisation of the IEEE Congress on Evolutionary Computation (CEC) 2017 and describe the main advantages and drawbacks of the use of each kind of test and put forward some recommendations concerning their use.