= Video Tutorials =

Video tutorials show how to use the HeuristicLab environment to complete tasks such as experiment analysis, algorithm prototyping or solving regression problems.


== Basic Tutorials ==

=== [wiki:Documentation/VideoTutorials/UsabilityViews Usability and Views] ===
This video demonstrates the basic user interface concepts of HeuristicLab and how to effectively use them.

=== [wiki:Documentation/VideoTutorials/ExecuteAlgorithms How to Execute Algorithms] ===
The video shows how you can parameterize and execute algorithms in HeuristicLab.

=== [wiki:Documentation/VideoTutorials/ExperimentDesign Experiment Design and Analysis] ===
This video shows how to create experiments and batch-runs in HeuristicLab as well as how to analyze the generated results.


== Advanced Tutorials ==

=== [wiki:Documentation/VideoTutorials/CustomAlgorithms How to Create Custom Algorithms] ===
This video shows how algorithms in HeuristicLab can be adapted in the GUI by extending a genetic algorithm to incorporate a crossover probability.

=== [wiki:Documentation/VideoTutorials/UserDefinedProblems How to Create User-defined Problems] ===
In this video it is shown how user-defined problems can be used to extend HeuristicLab with new custom optimization problems. As an example a user-defined n-queens problem is created and you can see how to define the problem's parameters and its solution encoding and how to implement a custom evaluation function using a programmable operator.

=== [wiki:Documentation/VideoTutorials/HeuristicLabHive How to Use HeuristicLab Hive] ===
Hive is HeuristicLab's distributed computing infrastructure and can be used to execute experiments in a massively parallel and distributed fashion. This video shows how to create an experiment, upload it to Hive for execution and view the results.

=== [wiki:Documentation/VideoTutorials/Scripting Rapid Prototyping Using the Scripting Environment] ===
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== Application-specific Tutorials ==

=== [wiki:Documentation/VideoTutorials/SymbolicRegression Symbolic Regression with HeuristicLab] ===
This tutorial covers the basic functionality for symbolic regression and for analyzing symbolic regression models in HeuristicLab.

First, we demonstrate how to load data and how to use genetic programming to produce symbolic regression models. After that, all charts and visualizations for symbolic regression models are shown and the functionality for model analysis, simplification, and tuning is explained in detail. At the end of this tutorial we show how symbolic regression models can be exported to MATLAB, LaTeX and Excel.

=== [wiki:Documentation/VideoTutorials/SymbolicClassification Symbolic Classification with HeuristicLab] ===
In this video tutorial the basic steps necessary to perform symbolic classification with HeuristicLab are covered. As exemplary data the mammography dataset from the UCI machine learning repository is chosen and modeled by genetic programming. You can see how the problem and the algorithm are configured and after the algorithm is finished the resulting model is analyzed.