计算机科学
非参数统计
群体智能
成对比较
集合(抽象数据类型)
计算智能
正态性
算法
参数统计
问题陈述
进化算法
独立性(概率论)
粒子群优化
人工智能
机器学习
统计假设检验
管理科学
统计
数学
经济
程序设计语言
作者
Joaquín Derrac,Salvador García,Daniel Molina,Francisco Herrera
标识
DOI:10.1016/j.swevo.2011.02.002
摘要
The interest in nonparametric statistical analysis has grown recently in the field of computational intelligence. In many experimental studies, the lack of the required properties for a proper application of parametric procedures–independence, normality, and homoscedasticity–yields to nonparametric ones the task of performing a rigorous comparison among algorithms. In this paper, we will discuss the basics and give a survey of a complete set of nonparametric procedures developed to perform both pairwise and multiple comparisons, for multi-problem analysis. The test problems of the CEC’2005 special session on real parameter optimization will help to illustrate the use of the tests throughout this tutorial, analyzing the results of a set of well-known evolutionary and swarm intelligence algorithms. This tutorial is concluded with a compilation of considerations and recommendations, which will guide practitioners when using these tests to contrast their experimental results.
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