Analyzing convergence performance of evolutionary algorithms: A statistical approach

作者：Derrac Joaquin; Garcia Salvador; Hui Sheldon; Suganthan Ponnuthurai Nagaratnam^*; Herrera Francisco

来源：Information Sciences, 2014, 289: 41-58.

DOI：10.1016/j.ins.2014.06.009

• Page%26apos
• s trend test
• Nonparametric tests
• Convergence-based algorithmic comparison
• Evolutionary algorithms

The analysis of the performance of different approaches is a staple concern in the design of Computational Intelligence experiments. Any proper analysis of evolutionary optimization algorithms should incorporate a full set of benchmark problems and state-of-the-art comparison algorithms. For the sake of rigor, such an analysis may be completed with the use of statistical procedures, supporting the conclusions drawn. %26lt;br%26gt;In this paper, we point out that these conclusions are usually limited to the final results, whereas intermediate results are seldom considered. We propose a new methodology for comparing evolutionary algorithms%26apos; convergence capabilities, based on the use of Page%26apos;s trend test. The methodology is presented with a case of use, incorporating real results from selected techniques of a recent special issue. The possible applications of the method are highlighted, particularly in those cases in which the final results do not enable a clear evaluation of the differences among several evolutionary techniques.

• 出版日期2014-12-24