Investigation of range extension with a genetic algorithm

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Range optimization is one of the tasks associated with the development of cost- effective, stand-off, air-to-surface munitions systems. The search for the optimal input parameters that will result in the maximum achievable range often employ conventional Monte Carlo techniques. Monte Carlo approaches can be time-consuming, costly, and insensitive to mutually dependent parameters and epistatic parameter effects. An alternative search and optimization technique is available in genetic algorithms. In the experiments discussed in this report, a simplified platform motion simulator was the fitness function for a genetic algorithm. The parameters to be optimized were the inputs to this motion generator and ... continued below

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8 p.

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Austin, A. S., LLNL March 4, 1998.

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Range optimization is one of the tasks associated with the development of cost- effective, stand-off, air-to-surface munitions systems. The search for the optimal input parameters that will result in the maximum achievable range often employ conventional Monte Carlo techniques. Monte Carlo approaches can be time-consuming, costly, and insensitive to mutually dependent parameters and epistatic parameter effects. An alternative search and optimization technique is available in genetic algorithms. In the experiments discussed in this report, a simplified platform motion simulator was the fitness function for a genetic algorithm. The parameters to be optimized were the inputs to this motion generator and the simulator`s output (terminal range) was the fitness measure. The parameters of interest were initial launch altitude, initial launch speed, wing angle-of-attack, and engine ignition time. The parameter values the GA produced were validated by Monte Carlo investigations employing a full-scale six-degree-of-freedom (6 DOF) simulation. The best results produced by Monte Carlo processes using values based on the GA derived parameters were within - 1% of the ranges generated by the simplified model using the evolved parameter values. This report has five sections. Section 2 discusses the motivation for the range extension investigation and reviews the surrogate flight model developed as a fitness function for the genetic algorithm tool. Section 3 details the representation and implementation of the task within the genetic algorithm framework. Section 4 discusses the results. Section 5 concludes the report with a summary and suggestions for further research.

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8 p.

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OSTI as DE98058763

Other: FDE: PDF; PL:

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  • 7. symposium on multidisciplinary analysis and optimization, St. Louis, MO (United States), 2-4 Sep 1998

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  • Other: DE98058763
  • Report No.: UCRL-JC--130305
  • Report No.: CONF-980909--
  • Grant Number: W-7405-ENG-48
  • Office of Scientific & Technical Information Report Number: 303927
  • Archival Resource Key: ark:/67531/metadc688605

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Reports, articles and other documents harvested from the Office of Scientific and Technical Information.

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  • March 4, 1998

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  • July 25, 2015, 2:20 a.m.

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  • April 6, 2017, 6:08 p.m.

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Austin, A. S., LLNL. Investigation of range extension with a genetic algorithm, article, March 4, 1998; California. (digital.library.unt.edu/ark:/67531/metadc688605/: accessed December 14, 2017), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.