The GA Procedure

Getting Started: GA Procedure

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:

1. Initialize the problem data, such as cost coefficients and parameter limits.
2. 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

3. Generate a population of solutions for the initial generation.
4. Control the execution of the algorithm and record your results.

The following sections discuss each of these items in detail.

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Initializing the Problem Data

Choosing the Problem Encoding

Setting the Objective Function

Controlling the Selection Process

Setting Crossover Parameters

Setting Mutation Parameters

Creating the Initial Generation

Monitoring Progress and Reporting Results

A Simple Example