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Fast converging elitist genetic algorithm for knot adjustment in B-spline curve approximation

  • Johannes Bureick EMAIL logo , Hamza Alkhatib and Ingo Neumann
Published/Copyright: August 23, 2019
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Abstract

B-spline curve approximation is a crucial task in many applications and disciplines. The most challenging part of B-spline curve approximation is the determination of a suitable knot vector. The finding of a solution for this multimodal and multivariate continuous nonlinear optimization problem, known as knot adjustment problem, gets even more complicated when data gaps occur. We present a new approach in this paper called an elitist genetic algorithm, which solves the knot adjustment problem in a faster and more precise manner than existing approaches. We demonstrate the performance of our elitist genetic algorithm by applying it to two challenging test functions and a real data set. We demonstrate that our algorithm is more efficient and robust against data gaps than existing approaches.

Award Identifier / Grant number: NE 1453/5-1

Funding statement: This work was partly funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) – NE 1453/5-1.

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Received: 2018-04-16
Accepted: 2019-08-07
Published Online: 2019-08-23
Published in Print: 2019-10-25

© 2019 Walter de Gruyter GmbH, Berlin/Boston

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