On the control landscape topology

Abstract : Evolutionary algorithms are powerful tools to optimize parameters and structure of control laws. However, these approaches are often very costly, or even prohibitive, for expensive experiments due to long evaluation times and large population sizes. Reducing the learning time, e.g. by decreasing the number of function evaluations, is a challenging problem as it often requires additional knowledge on the objective function and assumptions. We address the need to analyze these algorithms and guide their acceleration through examination of the search space topology and the exploratory and exploitative nature of the genetic operators. We show how this gives insights on the convergence and performance behavior of Genetic Programming Control for the drag reduction of a car model (Li et al., 2016). Profiling machine learning algorithms, that are very powerful but also more complex to analyze, aids the goal to increase their performance and making them eventually feasible for a wide range of applications.
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Contributor : Limsi Publications <>
Submitted on : Friday, August 10, 2018 - 1:36:44 PM
Last modification on : Saturday, May 4, 2019 - 1:19:42 AM

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  • HAL Id : hal-01856264, version 1

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Eurika Kaiser, Ruying Li, Bernd R. Noack. On the control landscape topology. World Congress of the International Federation of Automatic Control, Elsevier, Jul 2017, Toulouse, France. ⟨hal-01856264⟩

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