Introduction
Metaheuristic optimization algorithms are general optimization strategies suited to solve a range of real-world relevant optimization problems. Many metaheuristics expose parameters that allow to tune the effort that these algorithms are allowed to make and also the strategy and search behavior [1]. Adjusting these parameters allows to increase the algorithms’ performances with respect to different problem- and problem instance characteristics.
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Beham, A., Pitzer, E., Affenzeller, M. (2013). Fitness Landscape Based Parameter Estimation for Robust Taboo Search. In: Moreno-Díaz, R., Pichler, F., Quesada-Arencibia, A. (eds) Computer Aided Systems Theory - EUROCAST 2013. EUROCAST 2013. Lecture Notes in Computer Science, vol 8111. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-53856-8_37
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DOI: https://doi.org/10.1007/978-3-642-53856-8_37
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