@inproceedings{oai:jaxa.repo.nii.ac.jp:00006466,
 author = {Rousseau, Yannick and 中村, 佳朗 and Rousseau, Yannick and Nakamura, Yoshiaki},
 book = {宇宙航空研究開発機構特別資料: 航空宇宙数値シミュレーション技術シンポジウム2004論文集, JAXA Special Publication: Proceedings of Aerospace Numerical Simulation Symposium 2004},
 month = {Mar},
 note = {Genetic algorithms (GAs) have been successfully applied to numerical optimization problems, unfortunately GAs still remain computationally expensive, and the high computational cost make the use thereof impractical in most Aerodynamic optimization problems. Computational cost reduction is undoubtedly thought to be a common problem of most Aerodynamic shape optimization. In this approach, Artificial Neural Network (ANN) is used for function approximation; a number of mathematical computations are performed on the approximated function to obtain a reduced model. This resulting mathematical model is used to locate the variables that affect most the cost function. This approach is first tested on analytical functions, then numerical experimentations are conducted to solve shape optimization problem for the design of a wing profile. For each evaluation required by the optimizer, the Navier-Stokes equations with the Baldwin-Lomax turbulence model are solved., 資料番号: AA0048469026, レポート番号: JAXA-SP-04-012},
 pages = {155--159},
 publisher = {宇宙航空研究開発機構, Japan Aerospace Exploration Agency (JAXA)},
 title = {Acceleration of GA in aerodynamics by search space reduction and artificial neural},
 volume = {JAXA-SP-04-012},
 year = {2005}
}