The GA Procedure
- Overview
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Getting Started
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SyntaxPROC GA StatementContinueFor CallCross CallDynamic_array CallEvaluateLC CallGetDimensions CallGetObjValues CallGetSolutions CallInitialize CallMarkPareto CallMutate CallObjective FunctionPackBits CallProgramming StatementsReadChild CallReadCompare CallReadMember CallReadParent CallReEvaluate CallSetBounds CallSetCompareRoutine CallSetCross CallSetCrossProb CallSetCrossRoutine CallSetElite CallSetEncoding CallSetFinalize CallSetMut CallSetMutProb CallSetMutRoutine CallSetObj CallSetObjFunc CallSetProperty CallSetSel CallSetUpdateRoutine CallShellSort CallShuffle CallUnpackBits FunctionUpdateSolutions CallWriteChild CallWriteMember Call
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DetailsUsing Multisegment EncodingUsing Standard Genetic Operators and Objective FunctionsDefining a User Fitness Comparison RoutineDefining User Genetic OperatorsDefining a User Update RoutineDefining an Objective FunctionDefining a User Initialization RoutineSpecifying the Selection StrategyIncorporating Heuristics and Local OptimizationsHandling ConstraintsOptimizing Multiple Objectives
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Examples
- References
ReEvaluate Call
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call ReEvaluate ( <index> );
The ReEvaluate call reruns the evaluation phase of the genetic algorithm. The inputs to the ReEvaluate subroutine are as follows:
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index |
is a numeric scalar or array that specifies the indices of the solutions to be updated. The indices correspond to the order of the solutions obtained from a GetSolutions call. |
The ReEvaluate call recomputes the objective values for the current generation. You do not normally need to use this call, because the GA procedure evaluates the objective function during the optimization process in the evaluation phase. This subroutine should be called from a user update or finalize routine if a parameter that affects the objective value or solution is changed. The optional index parameter enables you to restrict the recomputation to the solution or subset of solutions specified. If the index parameter is not supplied, then the objective values of all the solutions will be recomputed.
For example, you might have a user objective function that can perform an additional local optimization if a particular parameter is set. If your update routine changes that parameter, then you should call the ReEvaluate subroutine to update the solutions and objective function values.