Optimize Parameters (Evolutionary)
Synopsis
This operator finds the optimal values of the selected parameters of the operators in its subprocess. It uses an evolutionary computation approach.
Description
This operator finds the optimal values for a set of parameters using an evolutionary approach which is often more appropriate than a grid search (as in the Optimize Parameters (Grid) operator) or a greedy search (as in the Optimize Parameters (Quadratic) operator) and leads to better results. This is a nested operator i.e. it has a subprocess. It executes its subprocess for a multiple number of times to find optimal values for the specified parameters.
This operator delivers the optimal parameter values through the parameter port which can also be written into a file with the legacy:write_parameters operator. This parameter set can be read in another process using the legacy:read_parameters operator. The performance vector for optimal values of parameters is delivered through the performance port. Any additional results of the subprocess are delivered through the result ports.
Other parameter optimization schemes are also available in Altair RapidMiner. The Optimize Parameters (Evolutionary) operator might be useful if the best ranges and dependencies are not known at all. Another operator which works similar to this parameter optimization operator is the Loop Parameters operator. In contrast to the optimization operators, this operator simply iterates through all parameter combinations. This might be especially useful for plotting purposes.
Differentiation
Optimize Parameters (Grid)
The Optimize Parameters (Grid) operator executes its subprocess for all combinations of the selected values of the parameters and then delivers the optimal parameter values.
Input
input
This operator can have multiple inputs. When one input is connected, another input port becomes available which is ready to accept another input (if any). The order of inputs remains the same. The Object supplied at the first input port of this operator is available at the first input port of the nested chain (inside the subprocess). Do not forget to connect all inputs in correct order. Make sure that you have connected the right number of ports at the subprocess level.
Output
performance
This port delivers the Performance Vector for the optimal values of the selected parameters. A Performance Vector is a list of performance criteria values.
parameter
This port delivers the optimal values of the selected parameters. This optimal parameter set can be written into a file with the legacy:write_parameters operator. The written parameter set can be read in another process using the legacy:read_parameters operator.
result
Any additional results of the subprocess are delivered through the result ports. This operator can have multiple outputs. When one result port is connected, another result port becomes available which is ready to deliver another output (if any). The order of outputs remains the same. The Object delivered at the first result port of the subprocess is delivered at the first result port of the operator. Don't forget to connect all outputs in correct order. Make sure that you have connected the right number of ports.
Parameters
edit parameter settings
The parameters are selected through the edit parameter settings menu. You can select the parameters and their possible values through this menu. This menu has an Operators window which lists all the operators in the subprocess of this operator. When you click on any operator in the Operators window, all parameters of that operator are listed in the Parameters window. You can select any parameter through the arrow keys of the menu. The selected parameters are listed in the Selected Parameters window. Only those parameters should be selected for which you want to find optimal values. This operator finds optimal values of the parameters in the specified range. The range of every selected parameter should be specified. When you click on any selected parameter (parameter in the Selected Parameters window) the Grid/Range option is enabled. This option allows you to specify the range of values of the selected parameters. The Min and Max fields are for specifying the lower and upper bounds of the range respectively. The steps and scale options are disabled for this operator. Note that only numerical parameters are displayed, since this operator does not support non numerical parameters.
error handling
This parameter allows you to select the method for handling errors occurring during the execution of the inner process. It has the following options:
- fail on error: In case an error occurs, the execution of the process will fail with an error message.
- ignore error: In case an error occurs, the error will be ignored and the execution of the process will continue with the next iteration.
max generations
This parameter specifies the number of generations after which the algorithm should be terminated.
use early stopping
This parameter enables early stopping. If not set to true, always the maximum number of generations are performed.
generations without improval
This parameter is only available when the use early stopping parameter is set to true. This parameter specifies the stop criterion for early stopping i.e. it stops after n generations without improvement in the performance. n is specified by this parameter.
specify population size
This parameter specifies the size of the population. If it is not set to true, one individual per example of the given ExampleSet is used.
population size
This parameter is only available when the specify population size parameter is set to true. This parameter specifies the population size i.e. the number of individuals per generation.
keep best
This parameter specifies if the best individual should survive. This is also called elitist selection. Retaining the best individuals in a generation unchanged in the next generation, is called elitism or elitist selection.
mutation type
This parameter specifies the type of the mutation operator.
selection type
This parameter specifies the selection scheme of this evolutionary algorithms.
tournament fraction
This parameter is only available when the selection type parameter is set to 'tournament'. It specifies the fraction of the current population which should be used as tournament members.
crossover prob
The probability for an individual to be selected for crossover is specified by this parameter.
use local random seed
This parameter indicates if a local random seed should be used for randomization. Using the same value of local random seed will produce the same randomization.
local random seed
This parameter specifies the local random seed. This parameter is only available if the use local random seed parameter is set to true.
show convergence plot
This parameter indicates if a dialog with a convergence plot should be drawn.