Changelog

Below you will find the detailed list of changes in every new release. For a more high level overview see the Whats new page.

5.0 - Released February 2008

A major new release with countless fixes, improvements, new sampling and modeling algorithms, and much more.
This release will also see a full rebranding. From hereon the M3-Toolbox will be known as the SUrrogate MOdeling Toolbox or SUMO Toolbox.

Fixed the 'Known bugs' for v4.1 (see Wiki)
Simulators can be passed options through an <Options> tag
Added a fixed model builder so you can manually force which model parameters to use
Removed ProActive dependency for the SGE distributed backend
Improved Makefile under unix/linux
Data produced by simulators no longer needs to be pre-scaled to [-1 1], this can be done automatically from the simulator configuration file
Deprecated the optimization framework. It is currently under re-design and a better, more integrated version, will be released with the next toolbox version.
Lots of cleanups, minor bugfixes and small feature enhancements

Fixed the 'Known bugs' for v4.0 (see Wiki)
Vastly improved test sample distribution if a test set is created on the fly
Gradient sample selector now works with complex outputs and has improved neighbourhood selection
Speed and usability improvements in the profiler framework
Improvements in the profiler DockedView widget (added a right click context menu)
Addition of some new examples
Added an option (on by default) that selects a certain percentage of the grid sample selector's points randomly, making the algorithm more robust
Some cleanups, minor bugfixes and feature enhancements

IMPORTANT: the best model score is now 0 instead of 1, this is more intuitive
Reworked and improved the model scoring mechanism, now based on a pareto analysis. This makes it possible to combine multpile measures in a sensible way.
Added a proof of concept surrogate driven optimization framework. Note this is an initial implementation which works, but don't expect state of the art results.
Cleanup and refactoring of the profiler framework
The profiling of model parameters has been totally reworked and this can now easily be tracked in a nice GUI widget
Cleanup of error function logic so you can now easily use different error functions (relative, RMS, ...) in the measures
Improved model plotting
Support for the SVMlight library (you must download it yourself in order to use it)
Added a MinMax measure which can be used to suppress spikes in rational models
Support for extinction prevention in the heterogenetic modeler
Fixed warnings (and in some cases errors) when loading models from disk
Respect the maximum running time more accurately
Many cleanups, minor bugfixes and feature enhancements

Fixed incorrect summary at the end of a run
Fixed bug due to duplicate sample points
Ability to evaluate multiple samples in parallel locally (support for dual/multi-core machines)
Speedups when reading in datasets
Added 2 new modelbuilders that optimize the parameters using;
- Pattern Search (requires the Matlab direct search toolbox)
- Simulated Annealing (requires Matlab v7.4 and the direct search toolbox)
- The Matlab Optimization Toolbox (includes different gradient based methods like BGFS)
A new density based sample selction algorithm (VoronoiSampleSelector)
New simulator examples to test with
Addition of a profiler to generate levelplots
Ability to generate Matlab API documentation using m2html
New neural network training algorithms based on Differential Evolution and Particle Swarm Optimization
It is now possible to call the toolbox with specific samples/values directly, e.g., go('myConfigFile.xml',xValues,yValues);
Many minor bugfixes and feature enhancements

Many important bugfixes
Documentation improvements
Fully working support for RBF models
New measure profilers that track the errors on measures
Many new predefined functions and datasets to test with. We now have over 50 examples!

Availability of pre-built binaries
Extensive refactoring and code cleanups
Many bugfixes and usability improvements
Resilience against simulator crashes
Ability to set the maximum running time for one sample evaluation
Vastly improved Genetic model builder + a neural network implementation
Addition of a RandomModelBuilder to use as a baseline benchmark
Possible to add dummy input variables or to model only a subset of the available inputs while clamping others
Improved multiple output support
- outputs can be modeled in parallel
- each output can be configured separately (eg. per output: model type, accuracy requirements (measure), sample selection algorithm, complex handling flag, etc)
- mutliple outputs can be combined into one model if the model type supports this
Noisy (gaussian, outliers, ...) versions of a given output can be automatically added
New and improved directory structure for output data
New model types:
- Kriging (based on the DACE MATLAB Kriging Toolbox by Lophaven, Nielsen and Sondergaard)
- Splines (based on the MATLAB Splines Toolbox, only for 1D and 2D)
Now matlab scripts can be used as datasources (simulators) as well
New initial experimental design
- Based on a dataset
- Combination of existing designs
- Based on the complexity of different 1D fits
Addition of new datasets and predefined functions as modeling examples