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].
The work described in this paper was done within the project “Connected Vehicles” which is funded by the European Fund for Regional Development (EFRE; further information on IWB/EFRE is available at www.efre.gv.at) and the country of Upper Austria.
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Werth, B., Pitzer, E., Affenzeller, M. (2020). Surrogate-Assisted Fitness Landscape Analysis for Computationally Expensive Optimization. In: Moreno-Díaz, R., Pichler, F., Quesada-Arencibia, A. (eds) Computer Aided Systems Theory – EUROCAST 2019. EUROCAST 2019. Lecture Notes in Computer Science(), vol 12013. Springer, Cham. https://doi.org/10.1007/978-3-030-45093-9_30
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