- author = "Eurika Kaiser and Ruiying Li and Bernd R. Noack",
- title = "On the control landscape topology",
- booktitle = "20th IFAC World Congress",
- year = "2017",
- editor = "Dimitri Peaucelle",
- pages = "Paper ThP23.4",
- address = "Toulouse, France",
- month = jul # " 9-14",
- organisation = "International Federation of Automatic Control",
- keywords = "genetic algorithms, genetic programming, Statistical data analysis, Evolutionary algorithms in control and identification, Knowledge discover (data mining), Information processing and decision support, control of fluid flows and fluids-structures interactions, evolutionary algorithms, machine learning control, proximity map, physics, mechanics of the fluids",
- bibsource = "OAI-PMH server at api.archives-ouvertes.fr",
- contributor = "Laboratoire d'Informatique pour la M{\'e}canique et les Sciences de l'Ing{\'e}nieur and Elsevier and Elsevier",
- identifier = "hal-01856264",
- language = "en",
- oai = "oai:HAL:hal-01856264v1",
- type = "info:eu-repo/semantics/conferenceObject",
-
URL = "
http://eurika-kaiser.com/ressources/articles/KaRuNo_2017_IFAC.pdf",
-
URL = "
http://www.ifac2017.org/sites/www.ifac2017.org/files/u88/IFAC17_ContentListWeb_4.html",
-
URL = "https://hal.archives-ouvertes.fr/hal-01856264",
- size = "5 pages",
- abstract = "Evolutionary algorithms are powerful tools to optimise 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 analyse 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 behaviour 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 analyse, aids the goal to increase their performance and making them eventually feasible for a wide range of applications.",
-
notes = "Author sometimes given as Ruying
Li
http://www.ifac2017.org/ oai:HAL:hal-01856264v1,",
