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Journal of Applied Geodesy

Editor-in-Chief: Kahmen, Heribert / Rizos, Chris


CiteScore 2018: 1.61

SCImago Journal Rank (SJR) 2018: 0.532
Source Normalized Impact per Paper (SNIP) 2018: 1.064

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ISSN
1862-9024
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Volume 13, Issue 4

Issues

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

Johannes Bureick / Hamza Alkhatib / Ingo Neumann
Published Online: 2019-08-23 | DOI: https://doi.org/10.1515/jag-2018-0015

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.

Keywords: B-spline curve; approximation; knot adjustment; genetic algorithm; data gaps; Monte Carlo

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About the article

Received: 2018-04-16

Accepted: 2019-08-07

Published Online: 2019-08-23

Published in Print: 2019-10-25


Funding Source: Deutsche Forschungsgemeinschaft

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

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


Citation Information: Journal of Applied Geodesy, Volume 13, Issue 4, Pages 317–328, ISSN (Online) 1862-9024, ISSN (Print) 1862-9016, DOI: https://doi.org/10.1515/jag-2018-0015.

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