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Deep Space Network Scheduling Using Evolutionary Computational MethodsThe paper presents the specific approach taken to formulate the problem in terms of gene encoding, fitness function, and genetic operations. The genome is encoded such that a subset of the scheduling constraints is automatically satisfied. Several fitness functions are formulated to emphasize different aspects of the scheduling problem. The optimal solutions of the different fitness functions demonstrate the trade-off of the scheduling problem and provide insight into a conflict resolution process.
Document ID
20070034024
Acquisition Source
Jet Propulsion Laboratory
Document Type
Preprint (Draft being sent to journal)
External Source(s)
Authors
Guillaume, Alexandre
(Jet Propulsion Lab., California Inst. of Tech. Pasadena, CA, United States)
Lee, Seugnwon
(Jet Propulsion Lab., California Inst. of Tech. Pasadena, CA, United States)
Wang, Yeou-Fang
(Jet Propulsion Lab., California Inst. of Tech. Pasadena, CA, United States)
Terrile, Richard J.
(Jet Propulsion Lab., California Inst. of Tech. Pasadena, CA, United States)
Date Acquired
August 24, 2013
Publication Date
March 3, 2007
Subject Category
Statistics And Probability
Meeting Information
Meeting: IEEE Aerospace Conference
Location: Big Sky, MT
Country: United States
Start Date: March 3, 2007
End Date: March 10, 2007
Sponsors: Institute of Electrical and Electronics Engineers
Distribution Limits
Public
Copyright
Other
Keywords
Deep Space Network (DSN)
scheduling
evolutionary computing

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