Surrogate-Assisted Fitness Landscape Analysis for Computationally Expensive Optimization

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Abstract

Exploratory fitness landscape analysis (FLA) is a category of techniques that try to capture knowledge about a black-box optimization problem. This is achieved by assigning features to a certain problem instance utilizing only information obtained by evaluating the black-box. This knowledge can be used to obtain new domain knowledge but more often the intended use is to automatically find an appropriate heuristic optimization algorithm [9]. FLA-based algorithm selection and parametrization hinges on the idea, that, while no optimization algorithm can be the optimal choice for all black-box problems, algorithms are expected to work similarly well on problems with similar statistical characteristics [8, 15].

OriginalspracheEnglisch
TitelComputer Aided Systems Theory – EUROCAST 2019 - 17th International Conference, Revised Selected Papers
Redakteure/-innenRoberto Moreno-Díaz, Alexis Quesada-Arencibia, Franz Pichler
Herausgeber (Verlag)Springer
Seiten247-254
Seitenumfang8
ISBN (Print)9783030450922
DOIs
PublikationsstatusVeröffentlicht - 2020
Veranstaltung17th International Conference on Computer Aided Systems Theory, EUROCAST 2019 - Las Palmas de Gran Canaria, Spanien
Dauer: 17 Feb. 201922 Feb. 2019

Publikationsreihe

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Band12013 LNCS
ISSN (Print)0302-9743
ISSN (elektronisch)1611-3349

Konferenz

Konferenz17th International Conference on Computer Aided Systems Theory, EUROCAST 2019
Land/GebietSpanien
OrtLas Palmas de Gran Canaria
Zeitraum17.02.201922.02.2019

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