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Bell-Curve Genetic Algorithm for Mixed Continuous and Discrete Optimization ProblemsIn this manuscript we have examined an extension of BCB that encompasses a mix of continuous and quasi-discrete, as well as truly-discrete applications. FVe began by testing two refinements to the discrete version of BCB. The testing of midpoint versus fitness (Tables 1 and 2) proved inconclusive. The testing of discrete normal tails versus standard mutation showed was conclusive and demonstrated that the discrete normal tails are better. Next, we implemented these refinements in a combined continuous and discrete BCB and compared the performance of two discrete distance on the hub problem. Here we found when "order does matter" it pays to take it into account.
Document ID
20030009797
Acquisition Source
Langley Research Center
Document Type
Conference Paper
Authors
Kincaid, Rex K.
(College of William and Mary Williamsburg, VA United States)
Griffith, Michelle
(College of William and Mary Williamsburg, VA United States)
Sykes, Ruth
(College of William and Mary Williamsburg, VA United States)
Sobieszczanski-Sobieski, Jaroslaw
(NASA Langley Research Center Hampton, VA United States)
Date Acquired
September 7, 2013
Publication Date
January 1, 2002
Subject Category
Computer Programming And Software
Report/Patent Number
AIAA Paper 2002-1675
Meeting Information
Meeting: 43rd AIAA Structures, Structural Dynamics and Materials
Location: Denver, CO
Country: United States
Start Date: April 22, 2002
End Date: April 25, 2002
Sponsors: American Inst. of Aeronautics and Astronautics
Funding Number(s)
CONTRACT_GRANT: NAG1-2077
Distribution Limits
Public
Copyright
Public Use Permitted.
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