Announcing GPdotNET v5 and related Book


https://www.igi-global.com/Images/Covers/9781522560050.png

After one year of writing and coding, finally I  can announce my two big achievements which are related to each other:

1. The fifth version of my open source project GPdotNET – genetic programming tool, and

2. The book:Optimized Genetic Programming Applications: Emerging Research and Opportunities, published by IGI-GLobal.

Along the book, I was developing GPdotNET application which is explained in Chapter 5. Actually the Chapter 5 described in depth all aspects of the application, with real world examples.

As can be seen GPdotNET v5 is completely rewritten application, with new logo and GUI. As Introduction of the application I have prepared several videos on youtube with quick explanation how to use some of the main modules in GPdotNET.

Announcement of GPdotNET v5 and ANNdotNET v1.0


As you already know GPdotNET v4 tool consists of several modules which include:

  • GP module for creating and training models based on genetic programming,
  • ANN module for creating and training models based on Feed Forward Neural Networks,
  • GA module for model and function optimization using Genetic Algorithm
  • LGA module is for  linear programming with GA which includes solving Traveling Salesman based problems, Assignment and Transportation problems.

With the latest release the GPdotNET has changed a lot. First of all, the initial idea about GPdotNET was to provide GP method in the application. And as the project grew lot of new implementations were included in the main project. This year I decided to make two different projects which can be seen as the natural evolution of GPdotNET v4.

The first project remain the same which follows the previous version and it is called GPdotNET v5. The project includes only GP related algorithm implementation which is developed for creating and training supervised ML problems (regression, binary and multi-class classification).

The second project uses several ANN algorithms for creating and training supervised machine learning problems.  The project is called ANNdotNET. It is Windows Forms desktop application very similar with GPdotNET, for creating and training ANN models.

I am very prod to announce that the new version of GPdotNET will be released as two  different open source projects.

gpdotnet-evolution

  1. GPdotNET v5 – which is hosted at the same address as previous. The older version GPdotNET v4 has moved at http://github.com/bhrnjica/gpdotnetv4  – and will be the latest version for non GP and ANN modules in GPdotNET.
  2. ANNdotNET v1 – is hosted at separate repository http://github.com/bhrnjica/anndotnet.

 

GPdotNET v4.0 Beta 3 has been released


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 http://github.com/bhrnjica/gpdotnet.

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