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|ErsterAutorNachname=Prothmann
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{{Publikation Author
 
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|Rank=2
 
|Author=Hartmut Schmeck
 
|Author=Hartmut Schmeck
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{{Publikation Author
 
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|Author=Holger Prothmann
 
 
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{{Inproceedings
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|Title=Evolutionary algorithms for traffic signal optimisation: A survey
 
|Title=Evolutionary algorithms for traffic signal optimisation: A survey
 
|Year=2009
 
|Year=2009
|Month=Mai
 
 
|Booktitle=Proceedings of mobil.TUM 2009 - International Scientific Conference on Mobility and Transport
 
|Booktitle=Proceedings of mobil.TUM 2009 - International Scientific Conference on Mobility and Transport
 
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reducing their time requirements are discussed. Furthermore, multi-objective evolutionary
 
reducing their time requirements are discussed. Furthermore, multi-objective evolutionary
 
algorithms that simultaneously treat several (contradicting) objectives are introduced.
 
algorithms that simultaneously treat several (contradicting) objectives are introduced.
|Projekt=OTC,OTC2
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|Forschungsgruppe=Effiziente Algorithmen
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{{Forschungsgebiet Auswahl
 
{{Forschungsgebiet Auswahl
 
|Forschungsgebiet=Evolutionäre Algorithmen
 
|Forschungsgebiet=Evolutionäre Algorithmen
 
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Version vom 8. September 2009, 11:23 Uhr


Evolutionary algorithms for traffic signal optimisation: A survey


Evolutionary algorithms for traffic signal optimisation: A survey



Published: 2009

Buchtitel: Proceedings of mobil.TUM 2009 - International Scientific Conference on Mobility and Transport

Referierte Veröffentlichung

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Kurzfassung
Evolutionary algorithms are optimisation heuristics that are inspired by biological evolution. They are relatively easy to comprehend and can be applied to any problem where a fitness function for rating candidate solutions is available. Therefore, evolutionary algorithms have been successfully applied to a wide range of real-world problems since their development in the 1960s. Since several years, their application domain also includes the optimisation of traffic signal systems. Here, the challenges are the often time-consuming and noisy fitness evaluations that are in many cases based on stochastic traffic simulations. The resulting time requirements make the use of evolutionary algorithms a challenging task especially in on-line scenarios where the traffic signal system has to be continuously adapted to changing traffic demands. This paper presents a structured overview of evolutionary algorithm applications in traffic signal optimisation. Different (off- and on-line) scenarios are presented and techniques for reducing their time requirements are discussed. Furthermore, multi-objective evolutionary algorithms that simultaneously treat several (contradicting) objectives are introduced.


Projekt

rTxrkXlk3OTC



Forschungsgebiet

Evolutionäre Algorithmen