(AI Studio Core)
Synopsis
This operator takes a performance vector as input and returns a performance vector containing the weighted fitness value of the specified criteria.Description
This Combine Performances operator takes a performance vector as input and returns a performance vector containing the weighted fitness value of the specified criteria. The user can specify the weights of different criteria. This operator takes the weighted average of the values of the specified criteria. It should be noted that some criteria values are considered positive by this operator e.g. accuracy. On the other hand some criteria values (usually error related) are considered negative by this operator e.g. relative error. Please study the attached Example Process for better understanding of this operator.
Input
performance (Performance Vector)This port expects a performance vector. A performance vector is a list of performance criteria values.
Output
- performance (Performance Vector)
The performance vector containing the weighted fitness value of the specified criteria is returned through this port.
Parameters
- default weightThis parameter specifies the default weight for all criteria that are not assigned a weight through the criteria weights parameter.
- criteria weightsDifferent performance criteria can be assigned different weights through this parameter. The criteria that are not assigned a weight through this parameter will have the default weight (i.e. specified by the default weight parameter).
Tutorial Processes
Introduction to the Combine Performances operator
This Example Process starts with the Subprocess operator. The subprocess is used for generating a sample performance vector. Therefore it is not necessary to understand the operators in the subprocess. A breakpoint is inserted after the Subprocess operator so that you can have a look at the performance vector. The performance vector has the following criteria values:
- Accuracy: 0.250
- Absolute error: 0.750
- Root mean squared error: 0.866
The Combine Performances operator is applied on this performance vector. Have a look at the criteria weights parameter of the Combine Performances operator. The following weights are assigned to criteria:
- Accuracy: 2.0
- Absolute error: 1.0
- Root mean squared error: 0.0
- (2(0.250) + 1(-0.750) + 0(0.866)) / 3
- = (0.500 - 0.750 + 0.000) / 3
- = -0.083
