Smooth curve fitting is the process of constructing a curve, or mathematical
function, that approximately fits a series of data points.
In this assignment, you are given set of points and you will use the genetic algorithm
to find the best coefficients to fit a curve (polynomial equation) to these points such
that the distance between the polynomial and the points is minimum.
http://en.wikipedia.org/wiki/Curve_fitting
Notes on what you must implement:
• Each coefficient is a floating point between [-10, 10].
• The fitness function is the mean square error (MSE). The best individual is
the one with the smallest fitness function because we want to minimize MSE.
• Use tournament selection.
• Use 2-point crossover.
• Use non-uniform mutation.
• Given a file of M data sets (i.e. M test cases), for each case, print and save
the list of coefficients and the total error. You must write the output to a file.Input File Structure:
• First line: M represents number of sets.
• Each set consists of: Line N D, where N is number of points and D is the
requested polynomial degree. This is followed by N lines each one
representing an (x, y) point.
• For example:
1
4 2
1 5
2 8
3 13
4 20
This example has 1 test case which has 4 points, and the requested degree is
2 (a0, a1, a2).
Output File Structure:
• Consists of M lines, each line has D+1 coefficients followed by “Error =” Total
Error.
• For example, for the above case, the output might be:
1.33, 0.12, 4.09, Error = 2.1563
