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Zaefferer, Martin ; Bartz-Beielstein, Thomas ; Naujoks, Boris ; Wagner, Tobias ; Emmerich, Michael:

Model-assisted Multi-criteria Tuning of an Event Detection Software under Limited Budgets

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Zitierfähiger Link: Bitte nutzen Sie diese URL, um auf das Dokument zu verlinken oder es zu zitieren:
http://nbn-resolving.de/urn:nbn:de:hbz:832-cos-234
URL: http://opus.bsz-bw.de/fhk/volltexte/2012/23/
Weitere beteiligte Personen (Hrsg. etc.): Bartz-Beielstein, Thomas
Institut: Fakultät 10 / Institut für Informatik
Dokumentart: Report (Bericht)
Sprache: Englisch
Erstellungsjahr: 2012
Publikationsdatum: 31.10.2012
Kurze Inhaltszusammenfassung auf Englisch Formerly, multi-criteria optimization algorithms were often tested
using tens of thousands function evaluations. In many real-world settings
function evaluations are very costly or the available budget is very limited.
Several methods were developed to solve these cost-extensive multi-criteria
optimization problems by reducing the number of function evaluations by
means of surrogate optimization. In this study, we apply different
multi-criteria surrogate optimization methods to improve (tune) an
event-detection software for water-quality monitoring. For tuning two
important parameters of this software, four state-of-the-art methods are
compared: S-Metric-Selection Efficient Global Optimization (SMS-EGO),
S-Metric-Expected Improvement for Efficient Global Optimization SExI-EGO,
Euclidean Distance based Expected Improvement Euclid-EI (here referred to
as MEI-SPOT due to its implementation in the Sequential Parameter
Optimization Toolbox SPOT) and a multi-criteria approach based on SPO
(MSPOT).

Analyzing the performance of the different methods provides insight into
the working-mechanisms of cutting-edge multi-criteria solvers. As one of
the approaches, namely MSPOT, does not consider the prediction variance
of the surrogate model, it is of interest whether this can lead to
premature convergence on the practical tuning problem. Furthermore, all
four approaches will be compared to a simple SMS-EMOA to validate that
the use of surrogate models is justified on this problem.
Kontrollierte Schlagwörter (Deutsch): Soft Computing , Optimierung , Mehrkriterielle Optimierung , Globale Optimierung , Modellierung
Freie Schlagwörter (Englisch): Event Detection , Water Quality Monitoring , Surrogate Optimization , Expected Improvement , Multi-criteria Optimization
DDC-Sachgruppe: Informatik
CCS - Klassifikation I.6 Simula , I.1.2 Algo , G.1.6 Opti
Schriftenreihe: CIplus
Bandnummer: 2/2012
ISBN: 2194-2870
Lizenz: Creative Commons - Namensnennung, Nicht kommerziell, Keine Bearbeitung