How experimental algorithmics can benefit from Mayo's extensions to Neyman-Pearson theory of testing

DOI10.1007/S11229-007-9297-ZzbMath1147.68074OpenAlexW2094140572MaRDI QIDQ935029

Thomas Bartz-Beielstein

Publication date: 31 July 2008

Published in: Synthese (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s11229-007-9297-z

zbMATH Keywords

OptimizationExperimental algorithmicsMayo's learning modelNew experimentalismSignificanceTheory of testing

Mathematics Subject Classification ID

Analysis of algorithms (68W40) Learning and adaptive systems in artificial intelligence (68T05) Problem solving in the context of artificial intelligence (heuristics, search strategies, etc.) (68T20) Testing in survival analysis and censored data (62N03)

Uses Software

• SPOT

Cites Work

• Experimental research in evolutionary computation. The new experimentalism
• The Future of Experimental Research
• Severe Testing as a Basic Concept in a Neyman–Pearson Philosophy of Induction
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