Project Description

GPdotNET is C# open source artificial intelligence tool for applying Genetic Algorithm and Artificial Neural Networks in modeling, prediction, optimization and pattern recognitions. With GPdotNET you can solve various engineering problems from classic regression and approximation to linear programming transportation and location problems and other machine learning based problems. 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, you can apply those methods in finding solutions. 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 machine learning mainly Artificial Neural Networks.

GPdotNET v4.0

Figure 1. GPdotNET v4 new look


The typical process of modelling with GPdotNET can be described in 5 steps.

  1.  Choosing the Solver Type: The first step is choosing the type of the solver. Which solver you will use depends on your intention what you want to do. For example if you want to make model for your experimental measurement you have several options which depend of your experimental data and the method you want to use. In GPdotNET you can use Genetic Programming or Neural Nets for modelling and prediction experimental data. But this is not strictly separate as may look on the flowchart below. That means that you can user Neural Networks for prediction, but training algorithm can be based on Genetic Algorithm or Particle Swarm Optimization or Back Propagation algorithm. 
  2. Loading Experimental Data: GPdotNET uses powerful tool for importing your experimental data regardless of the type of data. You can import your numerical, binary or classification data. GPdotNET can automatically define classes, or format numerical data with floating or comma separated decimal values. More info can be find in Section 2.
  3. Setting Learning Parameters. After data is loaded and prepared successfully, you have to set parameters for the selected method. GPdotNET providers various parameters for each method, so you can set parameters which can provides and generates best output model.
  4.  Searching for the solution: GPdotNET provides visualization of the searching solution so you can visually monitor how GPdotNET finds better solution as increasing the iteration number. If you provide data for testing calculated model, you can also see simulation of prediction.
  5. 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.


As can would be seen, 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 described previously are depicted in the graphical forms surrounded with Start and Stop elements.  

 Flowchart of modelling in GPdotNET v4



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 last iteration process start, or how much time is remain 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 Open source project

From developer point of view GPdotNET is .NET (Mono) application written in C# programming language which can run both on Windows and Linux based OS, or any OS which supports Mono framework. Project started in 2006 within postgraduate study for modeling and optimization with evolutionary algorithms. As open source project, GPdotNET is first published on November 5 2009 on The project is licensed under GNU Library General Public License (LGPL). For information about license and other kind of copyright please see The project is hosted at Main place for all news, documentation and code changes is my blog site at

How to citate GPdotNET

GPdotNET is used from all around the world, in scientific papers, journals, books, for diploma works, master thesis or Dissertations. It is free to use GPdotNET with proper citation. So if you want to use the GPdotNET you need the right way to citate the tool.

 Use this citation example in your paper, book or other material:

 [1] B. I. Hrnjica, GPdotNET V4.0- artificial intelligence tool [Computer program],, accessed {date}.


[1] Bahrudin I. Hrnjica, GPdotNET V4.0 – artificial intelligence tool [Computer program],, accessed {date}.


Documentation, Tutorials and Comments 

More info at my Blog:

Last edited Feb 20 at 7:06 AM by bhrnjica, version 40