Paper Info
Title
Towards better estimation of statistical significance when comparing evolutionary algorithms.
Abstract
The use of well-established statistical testing procedures to compare the performance of evolutionary algorithms often yields pessimistic results. This requires increasing the number of independent samples, and thus the computation time, in order to get results with the necessary precision. We aim at improving this situation by developing statistical tests that are good in answering typical questions coming from benchmarking of evolutionary algorithms. Our first step, presented in this paper, is a procedure that determines whether the performance distributions of two given algorithms are identical for each of the benchmarks. Our experimental study shows that this procedure is able to spot very small differences in the performance of algorithms while requiring computational budgets which are by an order of magnitude smaller (e.g. 15x) compared to the existing approaches.
Year
DOI
Venue
2019
10.1145/3319619.3326899
GECCO
Keywords
Field
DocType
Multiple comparisons, statistical significance
Evolutionary algorithm,Computer science,Artificial intelligence,Statistical significance,Machine learning
Conference
ISBN
Citations 
PageRank 
978-1-4503-6748-6
0
0.34
References 
Authors
0
1
Name
Order
Citations
PageRank
Maxim Buzdalov114125.29