Computing Nonlinear $\tau$-Estimation Based on Dynamic Differential Evolution Strategy

A novel algorithm named NTDDE to compute the nonlinear tau-estimation based on the dynamic differential evolution strategy is proposed in this letter. We construct a new updating stage for this dynamic differential evolution strategy to generate a population with better performance than before. The experimental evidence has been gathered to show that the proposed algorithm is capable of computing the nonlinear tau-estimation efficiently

[1]  J. Witmer,et al.  Nonlinear Regression Modeling. , 1984 .

[2]  A. Stromberg.  Computation of high breakdown nonlinear regression parameters , 1993 .

[3]  Amit Konar,et al.  Improved differential evolution algorithms for handling noisy optimization problems , 2005, 2005 IEEE Congress on Evolutionary Computation.

[4]  R. Storn,et al.  Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series) , 2005 .

[5]  V. Yohai,et al.  High Breakdown-Point Estimates of Regression by Means of the Minimization of an Efficient Scale , 1988 .

[6]  Anyong Qing,et al.  Dynamic differential evolution strategy and applications in electromagnetic inverse scattering problems , 2006, IEEE Trans. Geosci. Remote. Sens..

[7]  Arvind S. Mohais,et al.  DynDE: a differential evolution for dynamic optimization problems , 2005, 2005 IEEE Congress on Evolutionary Computation.

[8]  Ana Bianco,et al.  Approximate τ—Estimates for Linear Regression Based on Subsampling of Elemental Sets , 2001 .

[9]  Shinichi Sakata,et al.  S-estimation of nonlinear regression models with dependent and heterogeneous observations , 2001 .

[10]  Rainer Storn,et al.  Differential Evolution – A Simple and Efficient Heuristic for global Optimization over Continuous Spaces , 1997, J. Glob. Optim..

[11]  Shinichi Sakata,et al.  An Alternative Definition of Finite-Sample Breakdown Point with Applications to Regression Model Estimators , 1995 .

[12]  R. Storn,et al.  Differential Evolution - A simple and efficient adaptive scheme for global optimization over continuous spaces , 2004 .

[13]  B. Ripley,et al.  Robust Statistics , 2018, Encyclopedia of Mathematical Geosciences.

[14]  R. Storn,et al.  On the usage of differential evolution for function optimization , 1996, Proceedings of North American Fuzzy Information Processing.