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Options and Outputs

Running ga with the Default Options

To run the genetic algorithm with the default options, call ga with the syntax

[x,fval] = ga(@fitnessfun, nvars)

The input arguments to ga are

  • @fitnessfun — A function handle to the file that computes the fitness function. Compute Objective Functions explains how to write this file.

  • nvars — The number of independent variables for the fitness function.

The output arguments are

  • x — The final point

  • fval — The value of the fitness function at x

For a description of additional input and output arguments, see the reference page for ga.

You can run the example described in Minimize Rastrigin's Function from the command line by entering

rng(1,'twister') % for reproducibility
[x,fval] = ga(@rastriginsfcn,2)

This returns

Optimization terminated:
 average change in the fitness value less than options.FunctionTolerance.

x =
   -1.0421   -1.0018

fval =

Setting Options at the Command Line

You can specify any of the options that are available for ga by passing options as an input argument to ga using the syntax

[x,fval] = ga(@fitnessfun,nvars,[],[],[],[],[],[],[],options)

This syntax does not specify any linear equality, linear inequality, or nonlinear constraints.

You create options using the function optimoptions.

options = optimoptions(@ga)

This returns options with the default values for its fields.

options = 

  ga options:

   Set properties:
     No options set.

   Default properties:
             ConstraintTolerance: 1.0000e-03
                     CreationFcn: @gacreationuniform
                    CrossoverFcn: @crossoverscattered
               CrossoverFraction: 0.8000
                         Display: 'final'
                      EliteCount: '0.05*PopulationSize'
                    FitnessLimit: -Inf
               FitnessScalingFcn: @fitscalingrank
               FunctionTolerance: 1.0000e-06
                       HybridFcn: []
         InitialPopulationMatrix: []
          InitialPopulationRange: []
             InitialScoresMatrix: []
                  MaxGenerations: '100*numberOfVariables'
             MaxStallGenerations: 50
                    MaxStallTime: Inf
                         MaxTime: Inf
                     MutationFcn: {@mutationgaussian  [1]  [1]}
    NonlinearConstraintAlgorithm: 'auglag'
                       OutputFcn: []
                         PlotFcn: []
                  PopulationSize: '50 when numberOfVariables <= 5, else 200'
                  PopulationType: 'doubleVector'
                    SelectionFcn: @selectionstochunif
                     UseParallel: 0
                   UseVectorized: 0

The function ga uses these default values if you do not pass in options as an input argument.

The value of each option is stored in a field of options, such as options.PopulationSize. You can display any of these values by entering options followed by a period and the name of the field. For example, to display the size of the population for the genetic algorithm, enter


ans =

  1×40 char array

50 when numberOfVariables <= 5, else 200

To create options with a field value that is different from the default — for example to set PopulationSize to 100 instead of its default value 50 — enter

options = optimoptions('ga','PopulationSize',100)

This creates options with all values set to their defaults except for PopulationSize, which is set to 100.

If you now enter,


ga runs the genetic algorithm with a population size of 100.

If you subsequently decide to change another field in options, such as setting PlotFcn to @gaplotbestf, which plots the best fitness function value at each generation, call optimoptions with the syntax

options = optimoptions(options,'PlotFcn',@plotbestf)

This preserves the current values of all fields of options except for PlotFcn, which is changed to @plotbestf. Note that if you omit the input argument options, optimoptions resets PopulationSize to its default value 20.

You can also set both PopulationSize and PlotFcn with the single command

options = optimoptions('ga','PopulationSize',100,'PlotFcn',@plotbestf)

Additional Output Arguments

To get more information about the performance of the genetic algorithm, you can call ga with the syntax

[x,fval,exitflag,output,population,scores] = ga(@fitnessfcn, nvars)

Besides x and fval, this function returns the following additional output arguments:

  • exitflag — Integer value corresponding to the reason the algorithm terminated

  • output — Structure containing information about the performance of the algorithm at each generation

  • population — Final population

  • scores — Final scores

See the ga reference page for more information about these arguments.

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