Dynamic landscape analysis for open-ended stacking

Bernhard Werth; Johannes Karder; Andreas Beham; Stefan Wagner

doi:10.1145/3449726.3463153

Dynamic landscape analysis for open-ended stacking

Bernhard Werth, Johannes Karder, Andreas Beham, Stefan Wagner

Research output: Chapter in Book/Report/Conference proceedings › Conference contribution › peer-review

2 Citations (Scopus)

Abstract

Fitness landscape analysis (FLA) is a useful tool in the domain of (meta-)heuristic optimization but depends on explicitly knowing what fitness value is assigned to each solution. Dynamic optimization problems often do not provide their fitness landscape in such an explicit form, but by employing problem-specific knowledge, information about the problem itself and its current state can still be obtained. In this paper, a type of gray-box analysis of states of the open-ended stacking problem in two variations is presented. The current states obtained by monitoring the problem and algorithm during optimization are described via statistical measures similar to FLA measures. From this the distribution of possible states (the state landscape) and the transitions between problem states are analyzed. Visualization of the empirically obtained results reveals insights into algorithm-problem dynamics.

Original language	English
Title of host publication	GECCO 2021 Companion - Proceedings of the 2021 Genetic and Evolutionary Computation Conference Companion
Publisher	Association for Computing Machinery, Inc
Pages	1700-1707
Number of pages	8
ISBN (Electronic)	9781450383516
DOIs	https://doi.org/10.1145/3449726.3463153
Publication status	Published - 7 Jul 2021
Event	2021 Genetic and Evolutionary Computation Conference, GECCO 2021 - Virtual, Online, France Duration: 10 Jul 2021 → 14 Jul 2021

Publication series

Name	GECCO 2021 Companion - Proceedings of the 2021 Genetic and Evolutionary Computation Conference Companion

Conference

Conference	2021 Genetic and Evolutionary Computation Conference, GECCO 2021
Country/Territory	France
City	Virtual, Online
Period	10.07.2021 → 14.07.2021

Keywords

dynamic optimization
fitness landscape analysis
state space analysis

Access to Document

10.1145/3449726.3463153

Cite this

Werth, B., Karder, J., Beham, A., & Wagner, S. (2021). Dynamic landscape analysis for open-ended stacking. In GECCO 2021 Companion - Proceedings of the 2021 Genetic and Evolutionary Computation Conference Companion (pp. 1700-1707). (GECCO 2021 Companion - Proceedings of the 2021 Genetic and Evolutionary Computation Conference Companion). Association for Computing Machinery, Inc. https://doi.org/10.1145/3449726.3463153

Werth, Bernhard ; Karder, Johannes ; Beham, Andreas et al. / Dynamic landscape analysis for open-ended stacking. GECCO 2021 Companion - Proceedings of the 2021 Genetic and Evolutionary Computation Conference Companion. Association for Computing Machinery, Inc, 2021. pp. 1700-1707 (GECCO 2021 Companion - Proceedings of the 2021 Genetic and Evolutionary Computation Conference Companion).

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title = "Dynamic landscape analysis for open-ended stacking",

abstract = "Fitness landscape analysis (FLA) is a useful tool in the domain of (meta-)heuristic optimization but depends on explicitly knowing what fitness value is assigned to each solution. Dynamic optimization problems often do not provide their fitness landscape in such an explicit form, but by employing problem-specific knowledge, information about the problem itself and its current state can still be obtained. In this paper, a type of gray-box analysis of states of the open-ended stacking problem in two variations is presented. The current states obtained by monitoring the problem and algorithm during optimization are described via statistical measures similar to FLA measures. From this the distribution of possible states (the state landscape) and the transitions between problem states are analyzed. Visualization of the empirically obtained results reveals insights into algorithm-problem dynamics. ",

keywords = "dynamic optimization, fitness landscape analysis, state space analysis",

author = "Bernhard Werth and Johannes Karder and Andreas Beham and Stefan Wagner",

note = "Funding Information: The financial support by the Austrian Federal Ministry for Digital and Economic Affairs and the National Foundation for Research, Technology and Development is gratefully acknowledged. Publisher Copyright: {\textcopyright} 2021 ACM.; 2021 Genetic and Evolutionary Computation Conference, GECCO 2021 ; Conference date: 10-07-2021 Through 14-07-2021",

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month = jul,

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doi = "10.1145/3449726.3463153",

language = "English",

series = "GECCO 2021 Companion - Proceedings of the 2021 Genetic and Evolutionary Computation Conference Companion",

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Werth, B , Karder, J, Beham, A & Wagner, S 2021, Dynamic landscape analysis for open-ended stacking. in GECCO 2021 Companion - Proceedings of the 2021 Genetic and Evolutionary Computation Conference Companion. GECCO 2021 Companion - Proceedings of the 2021 Genetic and Evolutionary Computation Conference Companion, Association for Computing Machinery, Inc, pp. 1700-1707, 2021 Genetic and Evolutionary Computation Conference, GECCO 2021, Virtual, Online, France, 10.07.2021. https://doi.org/10.1145/3449726.3463153

Dynamic landscape analysis for open-ended stacking. / Werth, Bernhard ; Karder, Johannes; Beham, Andreas et al.
GECCO 2021 Companion - Proceedings of the 2021 Genetic and Evolutionary Computation Conference Companion. Association for Computing Machinery, Inc, 2021. p. 1700-1707 (GECCO 2021 Companion - Proceedings of the 2021 Genetic and Evolutionary Computation Conference Companion).

Research output: Chapter in Book/Report/Conference proceedings › Conference contribution › peer-review

TY - GEN

T1 - Dynamic landscape analysis for open-ended stacking

AU - Werth, Bernhard

AU - Karder, Johannes

AU - Beham, Andreas

AU - Wagner, Stefan

N1 - Funding Information: The financial support by the Austrian Federal Ministry for Digital and Economic Affairs and the National Foundation for Research, Technology and Development is gratefully acknowledged. Publisher Copyright: © 2021 ACM.

PY - 2021/7/7

Y1 - 2021/7/7

N2 - Fitness landscape analysis (FLA) is a useful tool in the domain of (meta-)heuristic optimization but depends on explicitly knowing what fitness value is assigned to each solution. Dynamic optimization problems often do not provide their fitness landscape in such an explicit form, but by employing problem-specific knowledge, information about the problem itself and its current state can still be obtained. In this paper, a type of gray-box analysis of states of the open-ended stacking problem in two variations is presented. The current states obtained by monitoring the problem and algorithm during optimization are described via statistical measures similar to FLA measures. From this the distribution of possible states (the state landscape) and the transitions between problem states are analyzed. Visualization of the empirically obtained results reveals insights into algorithm-problem dynamics.

AB - Fitness landscape analysis (FLA) is a useful tool in the domain of (meta-)heuristic optimization but depends on explicitly knowing what fitness value is assigned to each solution. Dynamic optimization problems often do not provide their fitness landscape in such an explicit form, but by employing problem-specific knowledge, information about the problem itself and its current state can still be obtained. In this paper, a type of gray-box analysis of states of the open-ended stacking problem in two variations is presented. The current states obtained by monitoring the problem and algorithm during optimization are described via statistical measures similar to FLA measures. From this the distribution of possible states (the state landscape) and the transitions between problem states are analyzed. Visualization of the empirically obtained results reveals insights into algorithm-problem dynamics.

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KW - state space analysis

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M3 - Conference contribution

AN - SCOPUS:85111047780

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BT - GECCO 2021 Companion - Proceedings of the 2021 Genetic and Evolutionary Computation Conference Companion

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Werth B , Karder J, Beham A, Wagner S. Dynamic landscape analysis for open-ended stacking. In GECCO 2021 Companion - Proceedings of the 2021 Genetic and Evolutionary Computation Conference Companion. Association for Computing Machinery, Inc. 2021. p. 1700-1707. (GECCO 2021 Companion - Proceedings of the 2021 Genetic and Evolutionary Computation Conference Companion). doi: 10.1145/3449726.3463153

Dynamic landscape analysis for open-ended stacking

Abstract

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Keywords

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JRZ adaptOp - Josef Ressel Center for Adaptive Optimization in Dynamic Environments

Cite this

Dynamic landscape analysis for open-ended stacking

Abstract

Publication series

Conference

Keywords

Access to Document

Other files and links

Fingerprint

Projects

JRZ adaptOp - Josef Ressel Center for Adaptive Optimization in Dynamic Environments

Cite this