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
Incorporating Heuristics and Local Optimizations
It is often effective to combine the genetic algorithm technique and other local optimizations or heuristic improvements. This can be done within the GA procedure by incorporating a local optimization into a user objective function and returning an improved objective value. Either your user objective function can replace the original solution with the optimized one, or you can leave the solution unchanged, replacing it with the optimized one only at the final iteration.
Replacing the original solution with the locally optimized one speeds convergence, but it also increases the risk of converging prematurely. If you choose to do so, you can modify the solution by writing the changed solution back to the population with a WriteMember call. You could also consider replacing the original solution with some probability p. For some problems, values of p from 0.05 to 0.15 have been shown to significantly improve convergence while avoiding premature convergence to a local optimum. This technique is illustrated in Example 3.1.