Genetic Algorithms samples
1D Function Optimization [Download]
This sample application demonstrates the very basics of Genetic Algorithms on the sample of 1D function optimization
(searching its maximum or minimum value)  one of the first samples, which is usually introduced by most books/courses
about Genetic Algorithms. It gives the basic introduction into such genetic concepts, like selection, crossover, mutation,
encoding problem's solution in chromosomes, etc.
Function Approximation [Download]
This sample goes deeper into genetic computations and introduces Genetic Programming (GP) and Gene Expression Programming (GEP).
Using both GP and GEP the sample application tries building an algebraic expression, which approximates the given function specified
as data points. For the approximation task, the application allows to specify functions set to use: only simple arithmetic operation
or extended set with additional functions. During algorithm's work, the application updates graph showing current solution, so it
could be seen how the found expression fits given data points.
Time Series Prediction [Download]
The time series prediction sample tries to solve yet another task with Genetic Programming and Gene Expression Programming. For the
given time series it tries to build an algebraic expression, which calculates next time series value from the given known past values.
Once a good expression is found during training phase, the expression may be tried to predict future data points from the last known values.
Traveling Salesman Problem [Download]
This sample application demonstrates solution of the famous Traveling Salesman Problem (TSP) using Genetic Algorithms.
The Traveling Salesman Problem requires finding the shortest path for visiting specified number of cities in such way,
that none of the cities is visit twice and after visiting the last city traveler returns back to the first one. This is
one of the famous NPhard problems, which would take extremely long time (if it is done by complete search) for finding
solution for a problem of relatively not that big size.
