Paper Info
Title
Curve fitting using coevolutionary genetic algorithms
Abstract
Curve fitting has many applications in lots of domains. The literature is full of fitting methods which are suitable for specific kinds of problems. In this paper we introduce a more general method to cover more range of problems. Our goal is to fit some cubic Bezier curves to data points of any distribution and order. The curves should be good representatives of the points and be connected and smooth. Theses constraints and the big search space make the fitting process difficult. We use the good capabilities of the coevolutionary algorithms in large problem spaces to fit the curves to the clusters of the data. The data are clustered using hierarchical techniques before the fitting process.
Year
DOI
Venue
2011
10.1007/978-3-642-27242-4_24
SEMCCO (2)
Keywords
Field
DocType
cubic bezier curve,coevolutionary algorithm,fitting method,fitting process,good capability,curve fitting,data point,big search space,good representative,general method,coevolutionary genetic algorithm
Data point,Hierarchical clustering,Cluster (physics),Mathematical optimization,Curve fitting,Computer science,Bézier curve,Artificial intelligence,Machine learning,Genetic algorithm
Conference
Volume
ISSN
Citations 
7077
0302-9743
0
PageRank 
References 
Authors
0.34
11
3
Name
Order
Citations
PageRank
Nejat A. Afshar100.34
soryani2299.48
Adel Torkaman Rahmani313919.77