Parallel Hierarchical Genetic Algorithm for Scattered Data Fitting through B-Splines

Autor: Juan Gabriel Avina-Cervantes, Carlos H. Garcia-Capulin, Maria de Jesus Estudillo-Ayala, Raul E. Sanchez-Yanez, Jose Edgar Lara-Ramirez, Horacio Rostro-Gonzalez
Jazyk: angličtina
Rok vydání: 2019
Předmět:
0209 industrial biotechnology
Computer science
02 engineering and technology
lcsh:Technology
B-spline fitting
Set (abstract data type)
lcsh:Chemistry
020901 industrial engineering & automation
Genetic algorithm
0202 electrical engineering
electronic engineering
information engineering
genetic algorithm
General Materials Science
Instrumentation
lcsh:QH301-705.5
scattered data
Fluid Flow and Transfer Processes
Structure (mathematical logic)
Fitness function
parallel computing
lcsh:T
Process Chemistry and Technology
General Engineering
Construct (python library)
lcsh:QC1-999
Computer Science Applications
Data point
lcsh:Biology (General)
lcsh:QD1-999
lcsh:TA1-2040
Curve fitting
020201 artificial intelligence & image processing
Variety (universal algebra)
lcsh:Engineering (General). Civil engineering (General)
Algorithm
lcsh:Physics
Zdroj: Applied Sciences, Vol 9, Iss 11, p 2336 (2019)
Applied Sciences
Volume 9
Issue 11
ISSN: 2076-3417
Popis: Curve fitting to unorganized data points is a very challenging problem that arises in a wide variety of scientific and engineering applications. Given a set of scattered and noisy data points, the goal is to construct a curve that corresponds to the best estimate of the unknown underlying relationship between two variables. Although many papers have addressed the problem, this remains very challenging. In this paper we propose to solve the curve fitting problem to noisy scattered data using a parallel hierarchical genetic algorithm and B-splines. We use a novel hierarchical structure to represent both the model structure and the model parameters. The best B-spline model is searched using bi-objective fitness function. As a result, our method determines the number and locations of the knots, and the B-spline coefficients simultaneously and automatically. In addition, to accelerate the estimation of B-spline parameters the algorithm is implemented with two levels of parallelism, taking advantages of the new hardware platforms. Finally, to validate our approach, we fitted curves from scattered noisy points and results were compared through numerical simulations with several methods, which are widely used in fitting tasks. Results show a better performance on the reference methods.
Databáze: OpenAIRE
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