Bootstrap Performance Profiles in Stochastic Algorithms Assessment

• Type: Article in International Conference Proceedings Book
• Date: 2015

Authors

Costa, L (Author) Other; Espirito Santo, IACPE (Author) Other; oliveira, p (Author) ICBAS

Conference proceedings International

Title: PROCEEDINGS OF THE INTERNATIONAL CONFERENCE OF NUMERICAL ANALYSIS AND APPLIED MATHEMATICS 2014 (ICNAAM-2014)

International Conference on Numerical Analysis and Applied Mathematics (ICNAAM)

Rhodes, GREECE, SEP 22-28, 2014

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Other information

Authenticus ID: P-00G-BC1

DOI: 10.1063/1.4912429

Abstract (EN): Optimization with stochastic algorithms has become a relevant research field. Due to its stochastic nature, its assessment is not straightforward and involves integrating accuracy and precision. Performance profiles for the mean do not show the trade-off between accuracy and precision, and parametric stochastic profiles require strong distributional assumptions and are limited to the mean performance for a large number of runs. In this work, bootstrap performance profiles are used to compare stochastic algorithms for different statistics. This technique allows the estimation of the sampling distribution of almost any statistic even with small samples. Multiple comparison profiles are presented for more than two algorithms. The advantages and drawbacks of each assessment methodology are discussed.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 4