Details
| Original language | English |
|---|---|
| Pages (from-to) | 317-328 |
| Number of pages | 12 |
| Journal | Journal of Applied Geodesy |
| Volume | 13 |
| Issue number | 4 |
| Early online date | 23 Aug 2019 |
| Publication status | Published - 25 Oct 2019 |
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
- approximation, B-spline curve, data gaps, genetic algorithm, knot adjustment, Monte Carlo
ASJC Scopus subject areas
- Mathematics(all)
- Modelling and Simulation
- Engineering(all)
- Engineering (miscellaneous)
- Earth and Planetary Sciences(all)
- Earth and Planetary Sciences (miscellaneous)
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In: Journal of Applied Geodesy, Vol. 13, No. 4, 25.10.2019, p. 317-328.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Fast converging elitist genetic algorithm for knot adjustment in B-spline curve approximation
AU - Bureick, Johannes
AU - Alkhatib, Hamza
AU - Neumann, Ingo
N1 - Funding information: This work was partly funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) – NE 1453/5-1.
PY - 2019/10/25
Y1 - 2019/10/25
N2 - 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.
AB - 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.
KW - approximation
KW - B-spline curve
KW - data gaps
KW - genetic algorithm
KW - knot adjustment
KW - Monte Carlo
UR - http://www.scopus.com/inward/record.url?scp=85071568146&partnerID=8YFLogxK
U2 - 10.1515/jag-2018-0015
DO - 10.1515/jag-2018-0015
M3 - Article
AN - SCOPUS:85071568146
VL - 13
SP - 317
EP - 328
JO - Journal of Applied Geodesy
JF - Journal of Applied Geodesy
SN - 1862-9016
IS - 4
ER -