No Wasting Time with Anytime Algorithms

How to Benchmark in Noisy Optimization

Boeh, Ramona, 2023

Art der Arbeit Master Thesis
Auftraggebende
Betreuende Dozierende Hanne, Thomas
Keywords anytime algorithms, noisy optimization, Evolutionary Algorithms, benchmarking, Evolutionary Computation, Black Box Optimization
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A standardized, elaborated and well-argued performance metric for benchmarking anytime algorithms on noisy optimization problems is missing in the current benchmarking frameworks. This Thesis aims at developing the very as an artefact by following the Design Science Research methodology.
Existing noise-handling practices and benchmarking guidelines are reviewed. Benchmarking experiments including a test suite containing 13 artificial test functions are conducted with two state-of-the-art algorithms to assess the performance metric.
Based on the generated data and existing literature it is concluded that combining the loss value and the runtime as performance metric maximizes reliability, expressiveness and information content considering the relevance.
Studiengang: Business Information Systems (Master)
Vertraulichkeit: öffentlich
Art der Arbeit
Master Thesis
Autorinnen und Autoren
Boeh, Ramona
Betreuende Dozierende
Hanne, Thomas
Publikationsjahr
2023
Sprache der Arbeit
Englisch
Vertraulichkeit
öffentlich
Studiengang
Business Information Systems (Master)
Standort Studiengang
Olten
Keywords
anytime algorithms, noisy optimization, Evolutionary Algorithms, benchmarking, Evolutionary Computation, Black Box Optimization