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Nonlinear Least Square Regression by Adaptive Domain Method With Multiple Genetic Algorithms

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Title: Nonlinear Least Square Regression by Adaptive Domain Method With Multiple Genetic Algorithms
Authors: Tomioka, S. Browse this author →KAKEN DB
Nisiyama, S. Browse this author →KAKEN DB
Enoto, T. Browse this author
Issue Date: Feb-2007
Publisher: IEEE
Journal Title: IEEE Transactions on Evolutionary Computation
Volume: 11
Issue: 1
Start Page: 1
End Page: 16
Publisher DOI: 10.1109/TEVC.2006.876363
Abstract: 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
Rights: ©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.
Type: article
Appears in Collections:工学院・工学研究院 (Graduate School of Engineering / Faculty of Engineering) > 雑誌発表論文等 (Peer-reviewed Journal Articles, etc)

Submitter: 富岡 智

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