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GP Studio
2.60 is a powerful model
development environment that offers significant Genetic Programming features
hosted within an easily accessible user interface. The software package
enables the automatic creation of analytical models, with the models presented
in source code form. The underlying analytical engine is fully multi-core
and distributed computing enabled! Details of the GP Studio
software can be found by using the navigation pane on the left hand side of this
site.
What's
New (January 1, 2008)
GP Studio is now free! That's right, the full GP Studio
application can be downloaded and used free of charge, but comes with no
free on-demand support. I have decided to move to a paid support
model. If you desire my support to help with the application of the
software to a problem, I am available on a paid consulting basis.
Please review the Support link for details.
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Application Features
 | Distributed Computing |
 | Multi-Core Support |
 | Relational Database Storage
 | Ability to import from comma separated files |
 | Ability to import from semi-colon separated files,
including proper numerical localization |
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 | Data Visualization |
 | User ability to write building block functions |
 | User ability to write fitness evaluation functions |
 | Project based modeling |
 | Multiple modeling profiles within each project |
 | Program Visualization & Analysis
 | Source code as: C, C++, C#, Visual Basic.NET, Java and
Fortran |
 | Tabular results |
 | Graphical results |
 | Model diagram |
 | Integrated computing against multiple validation files |
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 | Highly responsive, multi-threaded user interface |
 | Batch Model Processing |
Genetic Programming Features
 | Genetic Operators
 | Reproduction |
 | Mutation |
 | Crossover |
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 | High Level Structures
 | Automatically Defined Functions |
 | Automatically Defined Loops |
 | Indexed Memory |
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 | Program Fitness
 | Raw (Absolute) Fitness |
 | R2 Correlation |
 | Hit Count |
 | Custom Fitness Functions (user ability to write
fitness tests) |
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 | Program Code Reduction Techniques
 | Multi-Objective (SPEA2 Implementation) |
 | Adaptive Parsimony Pressure |
 | Pruning/Editing |
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 | Population Initialization
 | Full |
 | Grow |
 | Ramped Half-n-Half |
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 | Reproduction
 | Tournament |
 | Greedy Over Selection |
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 | Random Integer and Floating constants |
 | User Defined Functions |
 | Time Series history of input parameters |
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