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
The optimization problem is described by using programming statements, which initialize problem data and specify the objective, genetic operators, and other optimization parameters. The programming statements are executed once, and are followed by a RUN statement to begin the optimization process. The GA procedure enables you to define subroutines and designate them to be called during the optimization process to calculate objective functions, perform genetic mutation or crossover operations, or monitor and control the optimization. All variables created within a subroutine are local to that routine; to access a global variable defined within the GA procedure, the subroutine must have a parameter with the same name as the variable.
To set up a genetic algorithm optimization, your program needs to perform the following steps:
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Initialize the problem data, such as cost coefficients and parameter limits.
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Specify five basic optimization parameters:
Encoding:
the general structure and form of the solution
Objective:
the function to be optimized
Selection:
how members of the current solution generation are chosen to propagate the next generation
Crossover:
how the attributes of parent solutions are combined to produce new offspring solutions
Mutation:
how random variation is introduced into the new offspring solutions to maintain genetic diversity
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Generate a population of solutions for the initial generation.
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Control the execution of the algorithm and record your results.
The following sections discuss each of these items in detail.