This page hosts the documentation of the SmartCGMS software architecture and all of its components.

The documentation is being updated to acommodate the recent improvements. Please, be patient, we are working on it...


The console application lets you to execute a configuration, or to optimize filter parameters. Note that some filters can modify the configuration at runtime. For example, Pattern-Prediction can learn during execution, while saving the learned patterns to the configuration. To save what it has learned, use the /save_configuration switch.

     Usage: console3 filename [/optimize=filter_index,parameters_name[ /solver=generations[,population[,solver_GUID]]]] [/save_configuration]

     The filename designates the configuration of an experimental setup. Usually, it is .ini file.

     The filter_index starts at zero. parameters_name is a string.

     Generations is the maximum number of generations to evolve/computational steps.
     Population is the population size, when applicable, or 0 to allow entering the solver_id.
     Solver_GUID is solver's id, formatted like {01274B08-F721-42BC-A562-0556714C5685}.
     Make no spaces around the , delimiters.
     Default solver is Halton-driven Meta-Differential Evolution, 1000 generations, 100 population size.

     Available solvers:
     {1B21B62F-7C6C-4027-89BC-687D8BD32B3C} - MT MetaDE
     {01274B08-F721-42BC-A562-0556714C5685} - Halton MetaDE
     {2332F9A7-39A2-4FD6-90D5-90B885201869} - RND MetaDE
     {93FFF43A-50E8-4C7B-82F0-F290DFF2089C} - Halton PSO
     {033D92B0-B49C-45D1-957F-57682D56ABD2} - Sequential Brute Force Scan
     {FA42286B-928C-47BD-AAF3-2FE47377466D} - Sequential Convex Scan
     {6BAD021F-6F68-4246-A4E6-7B1950CA71CB} - RumorOpt

More solvers can be available, depending on your configruation and library-availability. SmartCGMS can use the NLOpt and PaGMO libraries.