Details
| Originalsprache | Englisch |
|---|---|
| Seiten (von - bis) | 317-328 |
| Seitenumfang | 12 |
| Fachzeitschrift | Journal of Applied Geodesy |
| Jahrgang | 13 |
| Ausgabenummer | 4 |
| Frühes Online-Datum | 23 Aug. 2019 |
| Publikationsstatus | Veröffentlicht - 25 Okt. 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.
ASJC Scopus Sachgebiete
- Mathematik (insg.)
- Modellierung und Simulation
- Ingenieurwesen (insg.)
- Ingenieurwesen (sonstige)
- Erdkunde und Planetologie (insg.)
- Erdkunde und Planetologie (sonstige)
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in: Journal of Applied Geodesy, Jahrgang 13, Nr. 4, 25.10.2019, S. 317-328.
Publikation: Beitrag in Fachzeitschrift › Artikel › Forschung › 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 -