Announcing GPdotNET v5 and related Book

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.


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


GPdotNET v4.0 has been released

After almost two years of implementation, I am proud to announce the forth version of the Open source project called GPdotNET v4.0. The latest version completely implements Genetic Programming and Artificial Neural Network for supervised learning tasks in three kind of problems: regression, binary and multiclass classification. Beside supervised learning tasks, with GPdotNET you can solve several Linear Programming problems: Traveling Salesman, Assignment and Transportation problems. The source code and binaries can be download from Github page:

Figure 1. Main Window in GPdotNET v4.0


In 2006 the GPdotNET started as post-graduate semester project, where I was trying to implement simple C# program based on genetic programming. After successfully implemented console application, started to implement .NET Windows application to be easy to use for anyone who wants to build mathematical model from the data based on genetic programming method. In November 2009 GPdotNET became an open source project, by providing the source code and installer. Since then I have received hundreds of emails, feedbacks, questions and comments. The project was hosted on In 2016 I decided to move the project to GitHub for better collaboration and compatibility, and can be found at However, for backward compatibility, the old hosting site will be live as long as the would be live. Since the beginning of the development, my intention was that the GPdotNET would be cross-OS application which can be run on Windows, Linux and Mac. Since version 2, GPdotNET can be compiled against .NET and Mono, and can be run on any OS which has Mono Framework installed. Beside this fact, vast majority of users are using GPdotNET on Windows OS.

GPdotNET is primarily used on Academia by helping engineers and researchers in modelling and prediction various problems, from the air pollution, water treatment, rainfall prediction, to the various modelling of machining processes, electrical engineering, vibration, automotive industry etc. GPdotNET is used in more than ten doctoral dissertations (known to me) and master thesis, nearly hundreds paper used GPdotNET in some kind of calculation.

Modeling with GPdotNET (New in GPdotNET v4.0)

Working with GPdotNET requires the data. By providing the learning algorithms GPdotNET uses a data of the research or experimental measures to learn about the problem. The results of learning algorithms are analytical models which can describe or predict the state of the problem, or can recognize the pattern. GPdotNET is very easy to use, even if you have no deep knowledge of GA, GP or ANN. Appling those methods in finding solutions can be achieved very quickly. The project can be used in modeling any kind of engineering process, which can be described with discrete data, as well as in education during teaching students about evolutionary methods, mainly GP and GA, as well as Artificial Neural Networks.

Working in GPdotNET follows the same procedures regardless of the problem type. That means you have the same set of steps when modelling with Genetic Programming or Neural Networks. In fact, GPdotNET contains the same set of input dialogs when you try to solve Traveling Salesman Problem with Genetic Algorithm or if you try to solve handwriting recognition by using Backpropagation Neural Networks. All learning algorithms within GPdotNET share the same UI.

The picture below shows the flowchart of the modelling in GPdotNET. The five steps are depicted in the graphical forms surrounded with Start and Stop item.

Figure 2. Modelling layout in GPdotNET 4.0

After GPdotNET is started main window is show, and the modelling process can be started.

Choosing the Solver Type

The first step is choosing the type of the solver. Which solver you will use it depends on your intention what you want to do. Choosing solver type begins when you press “New” button, the “GPdotNET Model creation wizard” appear. Soler types are grouped in two categories. The first group (on the left side) contains models implemented prior to v4.0 version. It contains solvers which apply GP in modelling regression problems, and GP in optimization of the GP models. In addition, you can perform optimization of any analytically defined function by using “Optimization of the Analytic function”. Also, there are three linear programming problems which GPdotNET can solve using GA.

On the right side, there are two kind of solvers: GP or ANN, which are not limited to solve only regression. Both GP or ANN can build model for regression, binary or multi-class problems. Which type of problem GPdotNET will use, depends of the type of the output column data (label column).

Figure 3. Available model types

Loading Experimental Data (new in GPdotNET 4.0)

GPdotNET uses powerful tool for importing your experimental data regardless of the type. You can import numerical, binary or classification data by using Importing Data Wizard. With GPdotNET importing tools you can import any kind of textual data, with any kind of separation character.

Figure 4. Importing dataset dialog

After the data is imported in forms of columns and rows, GPdotNET implemented set of very simple controls which can perform very powerful feature engineering. For each loaded column, you can set several types of metadata: column name, column type (input, output, ignore), normalization type (minmax, gauss), and missing value (min, max, avg). With those options, you can achieve most of the modelling scenarios. Before “Start Modelling” minimum conditions must be achieved.

  • At least one column must be of “input” parameter type.
  • At least one column must be of “output” parameter type.

Which type of problem (regression, binary or multi class) will be used depends of the type of the output column. The following cases are considered:

  1. in case of regression problems ouput column must be of numeric type.
  2. in case of binary classinfication output column must be of binary type.
  3. in case of multi class classinfication output column must be of categorical type.

Figure 5. Defining metadata for training data set

When the column should not be part of the feature list, it can be easily ignored when the Column Type is set to “ignore“, or Param type is set to “string“.

Figure 6. Changing column type to binary

Change value of metadata by double click on the current value, select new values from available popup list. When you done with Feature Engineering press “Start Modelling” button and the process of modelling can be start.

Note: After you press Start Modelling button you can still change values of metadta, but after every change of the metadata values, Start Modelling button must be pressed.

Setting Learning Parameters

Figure 7. Setting parameters Dialog

After data is loaded and prepared successfully, you have to set parameters for the selected method. GPdotNET provides various parameters for each method, so you can set parameters which can provides and generates best output model. Every parameter is self-explanatory.

Searching for the solution

GPdotNET provides visualization of the searching solution so you can visually monitor how GPdotNET finds better solution as the iteration number is increasing. Beside searching simulation, GPdotNET provides instant result representation (only GP models), so any time the user can see what is the best solution, and how currently best solution is good against validate or predicted set of data. (Result and Prediction tabs).

Figure 8. Searching simulation in GPdotNET

Saving and exporting the results:

GPdotNET provides several options you can choose while exporting your solution. You can export your solution in Excel or text file, as well as in Wolfram Mathematica or R programming languages (GP Models only). In case of ANN model the result can se exported only to Excel.

Figure 9. Searching simulation in GPdotNET

Besides parameters specific to learning algorithm, GPdotNET provides set of parameters which control the way of how iteration process should terminates as well as how iteration process should be processed by means of parallelization to use the multicore processors. During the problem searching GPdotNET records the history, so you can see when the best solution is found, how much time pass since the last iteration process start, or how much time is remaining to finish currently running iteration process.

Due to the fact that GP is the method which requires lot of processing time, GPdotNET provides parallelization, which speed up the process of searching. Enabling or disabling the parallelization processing is just a click of the button.

GPdotNET Start Page

In case you have no data or just want to test the application, GPdotNET providers 15 data samples for demo purposes. All samples are grouped in problems specific groups: Approximation and Regressions, Binary Classification, Multi-class classification, Time series modelling and Linear Programming.

Figure 10. Modelling layout in GPdotNET

By click on appropriate link sample can be opened to see current result and parameter values. You can easily change parameter, press Run button and search for another solution. This is very handy to introduce with GPdotNET. In any time, you can stop searching and export current model or save current state of the program.

Final note: The project is licensed under GNU Library General Public License (LGPL). For information about license and other kind of copyright e.g. using the application in commercial purpose please see

In case you need to cite it in scientific paper or book please refer to

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

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.


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.


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.


The last picture shows the Best Chromosome (solution) for the Iris Flows Data Set :


GPdotNET v4.0 Beta 2 released

Download GPdotNET v4 beta2

The last few days I am preparing the new build for publishing of GPdotNET v4.0 which will include lot of new features. In the last post I have announced ANN modul and compleately new modul for preparing tha data for modelling. Here is a quick overview of the new features comming in this build:

  1. Since this build the GP modul is also integrated with the new way of data preparation. Now with the latest version of GPdotNET the user will have the same user  experience in modelling with GP and ANN.
  2. The big news for this build is ability for modelling classification problems (two-class as well ) with Genetic programming. Multy -classs GP solver will be released soon.
  3. Separation of the previous and new version. Both are included in the latest build.
  4. Disable protected operations.


As picture shows below you can choose models from prevous version on the left side. On the right side of the new model dialog, you can select modeling and prediction with ANN or GP.

GPdotNET v4.0 Beta 2

After you select the solver GPdotNET is ready to accept the data.

GPdotNET v4.0 Beta 2 02

From the previous blog post you can see more info about loading and handling data.  The same user experimence you can see regadles of the solver type (ANN or GP).

Beside this GP integration there are several bug fix which were reported from the users.

In GP solver the new feature has been added: Ability to disable protected operations. In the previuous version of GPdotNET protected operations (eg. /, log, ln, etc) are enabled in the model. Whenever operation was undefined for the current value. GPdotNET returned default value (0 or 1). So with protected operation the model is always defined. With protected operations we collect much good genetic material dufirng evolution. In case the option is disable any upprotected operation can discar the model. This option is available in new and previous GP solver.

GPdotNET v4.0 Beta 2 03

Features not implemented in this beta

1. Exporting GP/ANN model

2. Open/Save gpa file for new Solvers.

GPdotNET 4.0 first look: Classification with Neural Networks

After some time of implementation and testing, the new version of GPdotNET is out. Go to codeplex page and download it. This is huge step in development of this project, because it contains completely new module based on Artificial Neural Network and other optimization methods e.g. Particle Swarm Optimization. gpdotnetv4_firstlook1

Almost all aspects of the architecture are changed, or will be changed when this version would be released.

The new Experiment class will replace existing classes for handling experimental data in GA based models, which is not implemented yet. Also New start page will contain more pre-calculated examples.

For this beta here are the new features:

  1. New Start Page will be extended with new examples of Neural Nets : – binary classification, – multiclass classification and – regressions examples.
  2. Improved module for loading experimental data, which now supports nonnumeric data like categorical or binary data.
  3. Depending of the output column of loaded experimental data different learning algorithm is selected. For example if the column is of categorical type, GPdotNET selects Neural Net algorithm with Cross-Entropy and Particle Swarm optimization learning algorithm. If the output column is numerical, GPdotNET selects the Neural Nets with Backpropagation learning algorithm. Currently only two learning algorithms are implemented. More will be implemented in the next beta release.

Classification problem with GPdotNET

This topic will give quick tutorial how to model classification problem with GPdotNET by using Neural Nets. For this tutorial you need some experimental data which you can download from this location.

  • Open GPdotNET choose New Command
  • New Dialog pops up, check Artificial Neural Nets, and Press Ok Button.


After Solver Type selection, GPdotNET Creates “Load Experiment” Page in which you can load experimental data, and define the percentage of data for testing the model. gpdotnetv4_firstlook3

  • Press Load Data button.
  • New Popup dialog appears.
  • Press File button select the file you previously downloaded.
  • Check Semicolon and First Row Header check buttons.

Note: When the data is analyzed correctly you should see vertical line “|” between columns. Otherwise data will not be loaded correctly.

  • When the experimental data is formatted correctly, press the “Import Data” button.

The next step is preparing columns for modeling. gpdotnetv4_firstlook4

For each columns we have to set:

  • a) proper type of the column (numeric, categorical or binary),
  • b) type of the parameter (input, output or ignore)
  • c) normalization method (MinMax,Gauss or Custom normalization of the column values ).

To change Column Type, double click on the cell which shows the column type, combo box appears with list of available types.


  • To change Parameter Type, double click on the cell which shows the param type, combobox appears with list of available parameter types.
  • gpdotnetv4_firstlook6
  • To change Normalization Type, double click on the cell which shows MinMax value, combobox appears with list of available normalization types.


Note: you can set only one Output column. Put Parameter Type to Ignore if you want to skip column from modelling.

Now we have experimental data, and we can start modelling process. Before that we need to choose how much data will be treated for testing. Enter 10% for testing the data and press Start Modelling button.

Now we have more Pages:

1. Settings page for setting the parameters of the Neural Nets

2. Run page for simulation of searching solution

3. Prediction page which you can see how solution is good against testing data.

Settings Page


As you can see in Settings Page you can set various parameters for Neural Nets and Particle Swarm Optimization. Try to train the model with different parameters values. For this tutorial you can leave parameters as are.

Modeling Page

Modeling page contains two diagrams. The first diagram shows errors with respect of the iteration number which is very useful for monitoring the searching process. The second diagram which is below the previous shows current best solution (blue line) in comparison with the experimental data (red line). Also on the left side of the page, you can see several iteration number, error, and other information about searching process. gpdotnetv4_firstlook9

Prediction Page

Prediction page shows how current best model predict data. Predict page contains tabular and graphical representation of predicted data, which is compared with data for testing. gpdotnetv4_firstlook10

Features don’t work in this BETA

1. Exporting Neural Network model

2. Saving to gpa file Neural Network Models