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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 | ||
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|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. | ||
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− | + | |Forschungsgruppe=Effiziente Algorithmen | |
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|Forschungsgebiet=Evolutionäre Algorithmen | |Forschungsgebiet=Evolutionäre Algorithmen | ||
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Version vom 11. September 2009, 14:52 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
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.