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タイトル
  • en Nonlinear Least Square Regression by Adaptive Domain Method With Multiple Genetic Algorithms
作成者
アクセス権 open access
権利情報
  • en ©2007 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. IEEE, IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION, vol. 11-1, 2007, pp. 1-16.
主題
  • NDC 421
内容注記
  • Abstract en In conventional least square (LS) regressions for nonlinear problems, it is not easy to obtain analytical derivatives with respect to target parameters that comprise a set of normal equations. Even if the derivatives can be obtained analytically or numerically, one must take care to choose the correct initial values for the iterative procedure of solving an equation, because some undesired, locally optimized solutions may also satisfy the normal equation. In the application of genetic algorithms (GAs) for nonlinear LS, it is not necessary to use normal equations, and a GA is also capable of avoiding localized optima. However, convergence of population and reliability of solutions depends on the initial domain of parameters, similarly to the choice of initial values in the above mentioned method using the normal equation. To overcome this disadvantage of applying GAs for nonlinear LS, we propose to use an adaptive domain method (ADM) in which the parameter domain can change dynamically by using several real-coded GAs with short lifetimes. Through an example problem, we demonstrate improvements in terms of both the convergence and the reliability by ADM. A further merit in the proposed method is that it does not require any specialized knowledge about GAs or their tuning. Therefore, the nonlinear LS by ADM with GAs are accessible to general scientists for various applications in many fields
出版者 en IEEE
日付
    Issued2007-02
言語
  • eng
資源タイプ journal article
出版タイプ VoR
資源識別子 HDL http://hdl.handle.net/2115/20117
関連
  • isIdenticalTo DOI https://doi.org/10.1109/TEVC.2006.876363
収録誌情報
    • PISSN 1089-778X
      • en IEEE Transactions on Evolutionary Computation
      • 巻11 号1 開始ページ1 終了ページ16
ファイル
    • fulltext TEC11-1.pdf
    • 876.66 KB (application/pdf)
      • Issued2007-02
コンテンツ更新日時 2023-07-26