[BOOK] Design Optimization Based on 
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响应面方法与遗传算法结合,取其两者的优点.个人觉得是一种不错的方法,有兴趣者可研读.很不错的资料.
Keywords: Design Optimization; Genetic Programming; Response Surface Methodology
Abstract: This thesis addresses two problems arising in many real-life design optimization
applications: the high computational cost of function evaluations and the presence of
numerical noise in the function values. The response surface methodology is used to
construct approximations of the original model. A major difficulty in building highly
accurate response surfaces is the selection of the structure of an approximation function.
A methodology has been developed for the approximation model building using
genetic programming. It is implemented in a computer code introducing two new
features: the use of design sensitivity information when available, and the allocation and
evaluation of tuning parameters in separation from the evolutionary process. A
combination of a genetic algorithm and a gradient-based algorithm is used for tuning of
the approximation functions. The problem of the choice of a design of experiments in
the response surface methodology has been reviewed and a space-filling plan adopted.
The developed methodology and software have been applied to design
optimization problems with numerically simulated and experimental responses,
demonstrating their considerable potential. The applications cover the approximation of
a response function obtained by a finite element model for the detection of damage in
steel frames, the creation of an empirical model for the prediction of the shear strength
in concrete deep beams and a multicriteria optimization of the process of calcination of
Roman cement.
分4部分上传:
Design Optimization Based on Genetic Programming.part1
Keywords: Design Optimization; Genetic Programming; Response Surface Methodology
Abstract: This thesis addresses two problems arising in many real-life design optimization
applications: the high computational cost of function evaluations and the presence of
numerical noise in the function values. The response surface methodology is used to
construct approximations of the original model. A major difficulty in building highly
accurate response surfaces is the selection of the structure of an approximation function.
A methodology has been developed for the approximation model building using
genetic programming. It is implemented in a computer code introducing two new
features: the use of design sensitivity information when available, and the allocation and
evaluation of tuning parameters in separation from the evolutionary process. A
combination of a genetic algorithm and a gradient-based algorithm is used for tuning of
the approximation functions. The problem of the choice of a design of experiments in
the response surface methodology has been reviewed and a space-filling plan adopted.
The developed methodology and software have been applied to design
optimization problems with numerically simulated and experimental responses,
demonstrating their considerable potential. The applications cover the approximation of
a response function obtained by a finite element model for the detection of damage in
steel frames, the creation of an empirical model for the prediction of the shear strength
in concrete deep beams and a multicriteria optimization of the process of calcination of
Roman cement.
分4部分上传:
Design Optimization Based on Genetic Programming.part1
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