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Record 1 of 918
Deep Space Network Scheduling Using Evolutionary Computational Methods
External Online Source: hdl:2014/40315
Author and Affiliation:
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)
Abstract: The 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.
Publication Date: Mar 03, 2007
Document ID:
20070034024
(Acquired Nov 15, 2007)
Subject Category: STATISTICS AND PROBABILITY
Document Type: Preprint
Meeting Information: IEEE Aerospace Conference; 3-10 Mar. 2007; Big Sky, MT; United States
Meeting Sponsor: Institute of Electrical and Electronics Engineers; United States
Financial Sponsor: Jet Propulsion Lab., California Inst. of Tech.; Pasadena, CA, United States
Organization Source: Jet Propulsion Lab., California Inst. of Tech.; Pasadena, CA, United States
Description: 4p; In English; Original contains color illustrations
Distribution Limits: Unclassified; Publicly available; Unlimited
Rights: Copyright
NASA Terms: DEEP SPACE NETWORK; GENETICS; TRADEOFFS; GENOME; CODING; SCHEDULING
Other Descriptors: DEEP SPACE NETWORK (DSN); SCHEDULING; EVOLUTIONARY COMPUTING
Availability Source: Other Sources
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