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|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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|Projekt=OTC, OTC2
 
|Forschungsgruppe=Effiziente Algorithmen
 
|Forschungsgruppe=Effiziente Algorithmen
 
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Aktuelle Version vom 6. April 2010, 09:32 Uhr


Evolutionary algorithms for traffic signal optimisation: A survey


Evolutionary algorithms for traffic signal optimisation: A survey



Published: 2009 Mai

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

Referierte Veröffentlichung

BibTeX

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

OTCOTC2



Forschungsgruppe

Effiziente Algorithmen


Forschungsgebiet

Evolutionäre Algorithmen