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This paper presents a system for user-assisted reverse modeling: from digitized point-cloud to solid models ready to be used in a CAD modeling system. Our approach consists in the following steps: segmentation, fitting, and constructive model discovery. Each of these steps are based on evolutionary algorithms. The obtained objects can then be further edited or parameterized by users and fitted to...
Based on unique advantage of genetic programming on solving linear problems, it was introduced into mining subsidence. By using relevant data of mining subsidence and the method of genetic programming, the nonlinear relation between maximum amount of mining subsidence and its influencing factors was set up. Compared with practical data, the results showed that the prediction accuracy of mining subsidence...
Multiclass classification problems arise naturally in many tasks in computer vision; typical examples include image segmentation and letter recognition. These are among some of the most challenging and important tasks in the area and solutions to them are eagerly sought after. Genetic programming (GP) is a powerful and flexible machine learning technique that has been successfully applied to many...
In this paper, a novel genetic programming named linear genetic programming with reusable gene (LGPRG) has been proposed. This new method absorbed the merits of many other linear genetic programming. It codes with a simple, nearly unrestrained string. Based on its character of reused, more expressions could be contained in one chromosome without the increase of computation task.Further more, the expression...
Tree encodings of programs are well known for their representative power and are used very often in Genetic Programming. In this paper we experiment with a new data structure, named straight line program (slp), to represent computer programs. The main features of this structure are described and new recombination operators for GP related to slp's are introduced. Experiments have been performed on...
To achieve high accuracy while lowering false alarm rates are major challenges in designing an intrusion detection system. In addressing this issue, this paper proposes an ensemble of one-class classifiers where each uses different learning paradigms. The techniques deployed in this ensemble model are; linear genetic programming (LGP), adaptive neural fuzzy inference system (ANFIS) and random forest...
The following topics are dealt with: optimal hybrid control for switched affine systems; multi-paradigm modeling for hybrid dynamic systems; polynomial methods for design of adaptive decentralized control; design and implementation of Scilab fuzzy logic toolbox; robust controller design using interval Diophantine equation; real-time interactive Simulink-based telelab; project-oriented approach to...
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