# Blog Archives

## 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
*

# Introduction

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:

- in case of
problems ouput column must be of*regression*.*numeric type* - in case of
classinfication output column must be of*binary*type.*binary* - in case of
classinfication output column must be of*multi class*type.*categorical*

*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 2 released

Download

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:

- 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.
- 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.
- Separation of the previous and new version. Both are included in the latest build.
- 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.

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

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.

## Features not implemented in this beta

1. **Exporting GP/ANN model**

**2. Open/Save gpa file for new Solvers.**

## GPdotNET v4.0- Introduction

*This is the first post in series of posts that describe upcomming version of v4.0. Besides lot of improvements of the current version, the main part isimplementation of neural networks and set of other optimization methods of machine learning.
*

## 1. Modelling with GPdotNET v4.0

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.

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

- Choosing the type of the Solver: 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.
- Loading your 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.
- 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.
- 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.
- 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 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.

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 started, 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.

### 1.1 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 codeplex.com. 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 .

### 1.2 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 etc.:

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

Or

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

## Announcing Neural Networks in GPdotNET

Last few months I was playing with Artificial Neural Network (ANN) and how to implement it in to the GPdotNET. ANN is one of the most popular methods in Machine Learning, specially Back Propagation algorithm. First of all, the Artificial Neural Network is more complex than Genetic Algorithm, and you need to dive deeper in to math and other related fields in order to understand some of the core concept of the ANN. But likely there are tons of fantastic learning sources about ANN. Here is my recommendation of ANN learning sources:

First of all there are several MSDN Magazine articles about ANN and how to implement it in C#.

1.

2.

3.

If you want to know what’s behind the scene of ANN, read this fantastic online book with great animations of how neuron and neural networks work.

1. , by Michael Nielsen.

There is a series you tube video about ANN.

1.

Open source libraries about ANN in C#:

1. . – Computer Vision, Artificial Intelligence, Robotics.

2. – Common Machine Learning Algorithms by

The first GPdotNET v4.0 beta will be out very soon.