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.

OriginalspracheEnglisch
TitelGECCO 2021 Companion - Proceedings of the 2021 Genetic and Evolutionary Computation Conference Companion
Herausgeber (Verlag)Association for Computing Machinery, Inc
Seiten1700-1707
Seitenumfang8
ISBN (elektronisch)9781450383516
DOIs
PublikationsstatusVeröffentlicht - 7 Jul 2021
Veranstaltung2021 Genetic and Evolutionary Computation Conference, GECCO 2021 - Virtual, Online, Frankreich
Dauer: 10 Jul 202114 Jul 2021

Publikationsreihe

NameGECCO 2021 Companion - Proceedings of the 2021 Genetic and Evolutionary Computation Conference Companion

Konferenz

Konferenz2021 Genetic and Evolutionary Computation Conference, GECCO 2021
Land/GebietFrankreich
OrtVirtual, Online
Zeitraum10.07.202114.07.2021

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