This is the third beta version of the GPdotNET v4.0 which brings new features and continuation of the new set of solvers. Beta 3 introduce Genetic Programming Multi-class Solver (GPMCS).
The latest version of the project can be found at .
As announced in Beta 1 and Beta 2 there are new set of solvers. Beta 3 finally brings GP Multi-class solver, and announced feature complete of the GPdotNET v4.0.
Here is a quick recap of all new features announced in the last three beta versions:
1. New Start Page will be extended with new examples of Neural Nets : – binary classification, – multi-class classification and – regressions examples.
2. Improved module for loading experimental data, which now supports non numeric data like categorical or binary data. New data module also support normalization of the experimental data, handling missing values.
3. Introduction of the ANN solver for all three kind of problems:
- regression
- binary
- multi-class
4. Depending of the output column of loaded experimental data, different learning solver can be selected.
5. Introduction of the GP Binary class solver.
6. Introduction of the GP Multi-Class solver. In the flowing text you can see few screen shots:
Picture below shows loaded “iris flower data set” in to the GP multi-class solver.
![gp_multyclass_sl0]()
The picture below shows GP Multi-class solver in action. As can be seen best solution is found at 242 iteration, with very high value of the fitness value.
![gp_multyclass_sl2]()
Even better the prediction page shows how best chromosome predict iris value. As can be seen best solution predicts 13 rows correctly, and only 2 row are calculated wrong.
![gp_multyclass_sl1]()
The last picture shows the Best Chromosome (solution) for the Iris Flows Data Set :
![gp_multyclass_sl3]()