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
Before executing a genetic algorithm, you must specify the objective function to be optimized. Either you can define a function in your GA procedure input and designate it to be your objective function with the SetObjFunc call, or you can specify an objective function that the GA procedure provides with a SetObj call. The GA procedure currently supports the traveling salesman problem objective.