Add Modeling Algorithm - Revision history

Admin at 08:04, 17 March 2014

2014-03-17T08:04:57Z

← Older revision		Revision as of 08:04, 17 March 2014
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	* '''OptimizerModelBuilder''': uses any algorithm that is implemented as part of the [[Optimizer]] hierarcy. A list of the optimizer configurations used in the default.xml can be found [[Config:Optimizer\|here]].		* '''OptimizerModelBuilder''': uses any algorithm that is implemented as part of the [[Optimizer]] hierarcy. A list of the optimizer configurations used in the default.xml can be found [[Config:Optimizer\|here]].
	* '''GeneticModelBuilder''': a genetic algorithm is used to search the parameter space. Given an initial population the algorithm uses crossover and mutation operators to search through the parameter space. (requires the [http://www.mathworks.com/products/gads/ Matlab Direct Search Toolbox (GADS) toolbox]). Note that the GeneticModelBuilder has two possible population types: custom and doubleVector. Selecting doubleVector will pass the hyperparameters directly to the GADS toolbox. This allows you to use the mutation and crossover operators built-in the GADS toolbox. Using the custom option will pas the models itself to the GADS toolbox. This requires custom crossover and mutation operators for the model type you are using, which should be defined in the ModelFactory class for this model.		* '''GeneticModelBuilder''': a genetic algorithm is used to search the parameter space. Given an initial population the algorithm uses crossover and mutation operators to search through the parameter space. (requires the [http://www.mathworks.com/products/gads/ Matlab Direct Search Toolbox (GADS) toolbox]). Note that the GeneticModelBuilder has two possible population types: custom and doubleVector. Selecting doubleVector will pass the hyperparameters directly to the GADS toolbox. This allows you to use the mutation and crossover operators built-in the GADS toolbox. Using the custom option will pas the models itself to the GADS toolbox. This requires custom crossover and mutation operators for the model type you are using, which should be defined in the ModelFactory class for this model.
	* '''~~AdaptiveModelBuilder~~''': this is the base class, this will build a single model each iteration according to the configuration set in the xml file. Thus there is no optimization, but it allows for full manual control		* '''ModelBuilder''': this is the base class, this will build a single model each iteration according to the configuration set in the xml file. Thus there is no optimization, but it allows for full manual control
	* '''ParetoModelBuilder''': uses the [[Multi-Objective Modeling\| multi-objective NSGA-II algorithm]] to generate models multi-objectively		* '''ParetoModelBuilder''': uses the [[Multi-Objective Modeling\| multi-objective NSGA-II algorithm]] to generate models multi-objectively
	* '''EGOModelBuilder''': uses an algorithm based on the [http://portal.acm.org/citation.cfm?id=596218 Efficient Global Optimization algorithm] to search for good model parameters. Basically this uses a nested kriging model to predict where to find the most promising models		* '''EGOModelBuilder''': uses an algorithm based on the [http://portal.acm.org/citation.cfm?id=596218 Efficient Global Optimization algorithm] to search for good model parameters. Basically this uses a nested kriging model to predict where to find the most promising models
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	If you would like to add a new optimization algorithm for optimizing the model parameters, you usually will have to do nothing more than [[add a new optimizer]] class. Then you can simply plug that optimizer into the OptimizerModelBuilder. Use one of the existing optimizers as an example to guide you.		If you would like to add a new optimization algorithm for optimizing the model parameters, you usually will have to do nothing more than [[add a new optimizer]] class. Then you can simply plug that optimizer into the OptimizerModelBuilder. Use one of the existing optimizers as an example to guide you.

	Alternatively, if you prefer to write your own algorithm from scratch you just have to derive from ~~AdaptiveModelBuilder~~ and provide the following methods:		Alternatively, if you prefer to write your own algorithm from scratch you just have to derive from ModelBuilder and provide the following methods:

	* a constructor that reads out the xml configuration and configures the modeling algorithm (MyModelBuilder.m)		* a constructor that reads out the xml configuration and configures the modeling algorithm (MyModelBuilder.m)

Mnguyen: /* Adding a new model builder */

2012-02-21T13:26:28Z

Adding a new model builder

← Older revision		Revision as of 13:26, 21 February 2012
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	* a constructor that reads out the xml configuration and configures the modeling algorithm (MyModelBuilder.m)		* a constructor that reads out the xml configuration and configures the modeling algorithm (MyModelBuilder.m)
	* runLoop.m : this is the main loop of your algorithm, it gets the current available samples and builds new models according to your optimization algorithm		* runLoop.m : this is the main loop of your algorithm, it gets the current available samples and builds new models according to your optimization algorithm
			* the optimized models are passed to the rest of Toolbox via the ''DefaultFitnessFunction'', which has to be called to in runLoop.m

	Once your algorithm is implemented you can then write the necessary factories for the model types you wish to use. See the [[Add Model Type]] page for more information. Make sure you think hard about the interfacing with the different model factories. Try to keep the number of methods that model factories need to implement as small as possible.		Once your algorithm is implemented you can then write the necessary factories for the model types you wish to use. See the [[Add Model Type]] page for more information. Make sure you think hard about the interfacing with the different model factories. Try to keep the number of methods that model factories need to implement as small as possible.

Mnguyen: /* Available Model Builders */

2012-01-30T16:33:00Z

Available Model Builders

← Older revision		Revision as of 16:33, 30 January 2012
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	A list of the available model builders can be found in the <code>src/matlab/modelbuilders</code> subdirectory. The most important are:		A list of the available model builders can be found in the <code>src/matlab/modelbuilders</code> subdirectory. The most important are:

	* '''OptimizerModelBuilder''': uses any algorithm that is implemented as part of the [[Optimizer]] hierarcy. A list of the optimizer configurations used in the default.xml can be found [[~~http://www.sumowiki.intec.ugent.be/index.php/~~Config:Optimizer\|here]].		* '''OptimizerModelBuilder''': uses any algorithm that is implemented as part of the [[Optimizer]] hierarcy. A list of the optimizer configurations used in the default.xml can be found [[Config:Optimizer\|here]].
	* '''GeneticModelBuilder''': a genetic algorithm is used to search the parameter space. Given an initial population the algorithm uses crossover and mutation operators to search through the parameter space. (requires the [http://www.mathworks.com/products/gads/ Matlab Direct Search Toolbox (GADS) toolbox]). Note that the GeneticModelBuilder has two possible population types: custom and doubleVector. Selecting doubleVector will pass the hyperparameters directly to the GADS toolbox. This allows you to use the mutation and crossover operators built-in the GADS toolbox. Using the custom option will pas the models itself to the GADS toolbox. This requires custom crossover and mutation operators for the model type you are using, which should be defined in the ModelFactory class for this model.		* '''GeneticModelBuilder''': a genetic algorithm is used to search the parameter space. Given an initial population the algorithm uses crossover and mutation operators to search through the parameter space. (requires the [http://www.mathworks.com/products/gads/ Matlab Direct Search Toolbox (GADS) toolbox]). Note that the GeneticModelBuilder has two possible population types: custom and doubleVector. Selecting doubleVector will pass the hyperparameters directly to the GADS toolbox. This allows you to use the mutation and crossover operators built-in the GADS toolbox. Using the custom option will pas the models itself to the GADS toolbox. This requires custom crossover and mutation operators for the model type you are using, which should be defined in the ModelFactory class for this model.
	* '''AdaptiveModelBuilder''': this is the base class, this will build a single model each iteration according to the configuration set in the xml file. Thus there is no optimization, but it allows for full manual control		* '''AdaptiveModelBuilder''': this is the base class, this will build a single model each iteration according to the configuration set in the xml file. Thus there is no optimization, but it allows for full manual control

Mnguyen: /* Available Model Builders */

2012-01-30T16:32:47Z

Available Model Builders

← Older revision		Revision as of 16:32, 30 January 2012
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	A list of the available model builders can be found in the <code>src/matlab/modelbuilders</code> subdirectory. The most important are:		A list of the available model builders can be found in the <code>src/matlab/modelbuilders</code> subdirectory. The most important are:

	* '''OptimizerModelBuilder''': uses any algorithm that is implemented as part of the [[Optimizer]] hierarcy.		* '''OptimizerModelBuilder''': uses any algorithm that is implemented as part of the [[Optimizer]] hierarcy. A list of the optimizer configurations used in the default.xml can be found [[http://www.sumowiki.intec.ugent.be/index.php/Config:Optimizer\|here]].
	* '''GeneticModelBuilder''': a genetic algorithm is used to search the parameter space. Given an initial population the algorithm uses crossover and mutation operators to search through the parameter space. (requires the [http://www.mathworks.com/products/gads/ Matlab Direct Search Toolbox (GADS) toolbox]). Note that the GeneticModelBuilder has two possible population types: custom and doubleVector. Selecting doubleVector will pass the hyperparameters directly to the GADS toolbox. This allows you to use the mutation and crossover operators built-in the GADS toolbox. Using the custom option will pas the models itself to the GADS toolbox. This requires custom crossover and mutation operators for the model type you are using, which should be defined in the ModelFactory class for this model.		* '''GeneticModelBuilder''': a genetic algorithm is used to search the parameter space. Given an initial population the algorithm uses crossover and mutation operators to search through the parameter space. (requires the [http://www.mathworks.com/products/gads/ Matlab Direct Search Toolbox (GADS) toolbox]). Note that the GeneticModelBuilder has two possible population types: custom and doubleVector. Selecting doubleVector will pass the hyperparameters directly to the GADS toolbox. This allows you to use the mutation and crossover operators built-in the GADS toolbox. Using the custom option will pas the models itself to the GADS toolbox. This requires custom crossover and mutation operators for the model type you are using, which should be defined in the ModelFactory class for this model.
	* '''AdaptiveModelBuilder''': this is the base class, this will build a single model each iteration according to the configuration set in the xml file. Thus there is no optimization, but it allows for full manual control		* '''AdaptiveModelBuilder''': this is the base class, this will build a single model each iteration according to the configuration set in the xml file. Thus there is no optimization, but it allows for full manual control

Mnguyen: /* Available Model Builders */

2012-01-30T13:36:55Z

Available Model Builders

← Older revision		Revision as of 13:36, 30 January 2012
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	* '''OptimizerModelBuilder''': uses any algorithm that is implemented as part of the [[Optimizer]] hierarcy.		* '''OptimizerModelBuilder''': uses any algorithm that is implemented as part of the [[Optimizer]] hierarcy.
	* '''GeneticModelBuilder''': a genetic algorithm is used to search the parameter space. Given an initial population the algorithm uses crossover and mutation operators to search through the parameter space. (requires the [http://www.mathworks.com/products/gads/ Matlab Direct Search Toolbox (GADS) toolbox]). Note that the GeneticModelBuilder has two possible population types: custom and doubleVector. Selecting doubleVector will ~~pas~~ the hyperparameters directly to the GADS toolbox. This allows you to use the mutation and crossover operators built-in the GADS toolbox. Using the custom option will pas the models itself to the GADS toolbox. This requires custom crossover and mutation operators for the model type you are using, which should be defined in the ModelFactory class for this model.		* '''GeneticModelBuilder''': a genetic algorithm is used to search the parameter space. Given an initial population the algorithm uses crossover and mutation operators to search through the parameter space. (requires the [http://www.mathworks.com/products/gads/ Matlab Direct Search Toolbox (GADS) toolbox]). Note that the GeneticModelBuilder has two possible population types: custom and doubleVector. Selecting doubleVector will pass the hyperparameters directly to the GADS toolbox. This allows you to use the mutation and crossover operators built-in the GADS toolbox. Using the custom option will pas the models itself to the GADS toolbox. This requires custom crossover and mutation operators for the model type you are using, which should be defined in the ModelFactory class for this model.
	* '''AdaptiveModelBuilder''': this is the base class, this will build a single model each iteration according to the configuration set in the xml file. Thus there is no optimization, but it allows for full manual control		* '''AdaptiveModelBuilder''': this is the base class, this will build a single model each iteration according to the configuration set in the xml file. Thus there is no optimization, but it allows for full manual control
	* '''ParetoModelBuilder''': uses the [[Multi-Objective Modeling\| multi-objective NSGA-II algorithm]] to generate models multi-objectively		* '''ParetoModelBuilder''': uses the [[Multi-Objective Modeling\| multi-objective NSGA-II algorithm]] to generate models multi-objectively

Mnguyen: /* Available Model Builders */

2011-06-30T09:14:07Z

Available Model Builders

← Older revision		Revision as of 09:14, 30 June 2011
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	* '''OptimizerModelBuilder''': uses any algorithm that is implemented as part of the [[Optimizer]] hierarcy.		* '''OptimizerModelBuilder''': uses any algorithm that is implemented as part of the [[Optimizer]] hierarcy.
	* '''GeneticModelBuilder''': a genetic algorithm is used to search the parameter space. Given an initial population the algorithm uses crossover and mutation operators to search through the parameter space. (requires the [http://www.mathworks.com/products/gads/ Matlab Direct Search Toolbox (GADS) toolbox]). Note that the GeneticModelBuilder has two possible population types: custom and doubleVector. Selecting doubleVector will pas the hyperparameters directly to the GADS toolbox. This allows you to use the mutation and crossover operators built-in the GADS toolbox. Using the custom option will pas the models itself to the GADS toolbox. This requires ~~that~~ custom crossover and mutation operators, which should be defined in the ModelFactory class for this model.		* '''GeneticModelBuilder''': a genetic algorithm is used to search the parameter space. Given an initial population the algorithm uses crossover and mutation operators to search through the parameter space. (requires the [http://www.mathworks.com/products/gads/ Matlab Direct Search Toolbox (GADS) toolbox]). Note that the GeneticModelBuilder has two possible population types: custom and doubleVector. Selecting doubleVector will pas the hyperparameters directly to the GADS toolbox. This allows you to use the mutation and crossover operators built-in the GADS toolbox. Using the custom option will pas the models itself to the GADS toolbox. This requires custom crossover and mutation operators for the model type you are using, which should be defined in the ModelFactory class for this model.
	* '''AdaptiveModelBuilder''': this is the base class, this will build a single model each iteration according to the configuration set in the xml file. Thus there is no optimization, but it allows for full manual control		* '''AdaptiveModelBuilder''': this is the base class, this will build a single model each iteration according to the configuration set in the xml file. Thus there is no optimization, but it allows for full manual control
	* '''ParetoModelBuilder''': uses the [[Multi-Objective Modeling\| multi-objective NSGA-II algorithm]] to generate models multi-objectively		* '''ParetoModelBuilder''': uses the [[Multi-Objective Modeling\| multi-objective NSGA-II algorithm]] to generate models multi-objectively

Mnguyen: /* Available Model Builders */

2011-06-30T09:13:36Z

Available Model Builders

← Older revision		Revision as of 09:13, 30 June 2011
Line 11:		Line 11:

	* '''OptimizerModelBuilder''': uses any algorithm that is implemented as part of the [[Optimizer]] hierarcy.		* '''OptimizerModelBuilder''': uses any algorithm that is implemented as part of the [[Optimizer]] hierarcy.
	* '''GeneticModelBuilder''': a genetic algorithm is used to search the parameter space. Given an initial population the algorithm uses crossover and mutation operators to search through the parameter space. (requires the [http://www.mathworks.com/products/gads/ Matlab Direct Search Toolbox (GADS) toolbox]). Note that the GeneticModelBuilder has two possible population types: custom and doubleVector. Selecting doubleVector will pas the hyperparameters directly to the GADS toolbox. This allows you to use the mutation and crossover operators built-in the toolbox. Using the custom option will pas the models itself to the GADS toolbox. This requires that crossover and mutation operators ~~are~~ defined in the ModelFactory class for this model.		* '''GeneticModelBuilder''': a genetic algorithm is used to search the parameter space. Given an initial population the algorithm uses crossover and mutation operators to search through the parameter space. (requires the [http://www.mathworks.com/products/gads/ Matlab Direct Search Toolbox (GADS) toolbox]). Note that the GeneticModelBuilder has two possible population types: custom and doubleVector. Selecting doubleVector will pas the hyperparameters directly to the GADS toolbox. This allows you to use the mutation and crossover operators built-in the GADS toolbox. Using the custom option will pas the models itself to the GADS toolbox. This requires that custom crossover and mutation operators, which should be defined in the ModelFactory class for this model.
	* '''AdaptiveModelBuilder''': this is the base class, this will build a single model each iteration according to the configuration set in the xml file. Thus there is no optimization, but it allows for full manual control		* '''AdaptiveModelBuilder''': this is the base class, this will build a single model each iteration according to the configuration set in the xml file. Thus there is no optimization, but it allows for full manual control
	* '''ParetoModelBuilder''': uses the [[Multi-Objective Modeling\| multi-objective NSGA-II algorithm]] to generate models multi-objectively		* '''ParetoModelBuilder''': uses the [[Multi-Objective Modeling\| multi-objective NSGA-II algorithm]] to generate models multi-objectively

Mnguyen: /* Available Model Builders */

2011-06-30T09:12:54Z

Available Model Builders

← Older revision		Revision as of 09:12, 30 June 2011
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	* '''OptimizerModelBuilder''': uses any algorithm that is implemented as part of the [[Optimizer]] hierarcy.		* '''OptimizerModelBuilder''': uses any algorithm that is implemented as part of the [[Optimizer]] hierarcy.
	* '''GeneticModelBuilder''': a genetic algorithm is used to search the parameter space. Given an initial population the algorithm uses crossover and mutation operators to search through the parameter space. (requires the [http://www.mathworks.com/products/gads/ Matlab Direct Search Toolbox (GADS) toolbox]). Note that the GeneticModelBuilder has two possible population types: custom and doubleVector.		* '''GeneticModelBuilder''': a genetic algorithm is used to search the parameter space. Given an initial population the algorithm uses crossover and mutation operators to search through the parameter space. (requires the [http://www.mathworks.com/products/gads/ Matlab Direct Search Toolbox (GADS) toolbox]). Note that the GeneticModelBuilder has two possible population types: custom and doubleVector. Selecting doubleVector will pas the hyperparameters directly to the GADS toolbox. This allows you to use the mutation and crossover operators built-in the toolbox. Using the custom option will pas the models itself to the GADS toolbox. This requires that crossover and mutation operators are defined in the ModelFactory class for this model.
	* '''AdaptiveModelBuilder''': this is the base class, this will build a single model each iteration according to the configuration set in the xml file. Thus there is no optimization, but it allows for full manual control		* '''AdaptiveModelBuilder''': this is the base class, this will build a single model each iteration according to the configuration set in the xml file. Thus there is no optimization, but it allows for full manual control
	* '''ParetoModelBuilder''': uses the [[Multi-Objective Modeling\| multi-objective NSGA-II algorithm]] to generate models multi-objectively		* '''ParetoModelBuilder''': uses the [[Multi-Objective Modeling\| multi-objective NSGA-II algorithm]] to generate models multi-objectively

Admin at 16:13, 8 February 2009

2009-02-08T16:13:11Z

Show changes

Dgorissen: /* FixedModelBuilder */

2008-07-25T12:25:50Z

FixedModelBuilder

← Older revision		Revision as of 12:25, 25 July 2008
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	=== FixedModelBuilder ===		=== FixedModelBuilder ===
	* a createFixedModel method that takes no argument and returns a model with a fixed set of parameters.		* a createFixedModel method that takes no argument and returns a model with a fixed set of parameters. This allows you to set the model parameters manually.

	== Using your model builder ==		== Using your model builder ==
	Having written the implementations you then have to update your xml configuration file to use you new model type, run 'go'.		Having written the implementations you then have to update your xml configuration file to use you new model type, run 'go'.