Documentation

Run ga from a File

The command-line interface enables you to run the genetic algorithm many times, with different options settings, using a file. For example, you can run the genetic algorithm with different settings for Crossover fraction to see which one gives the best results. The following code runs the function ga 21 times, varying options.CrossoverFraction from 0 to 1 in increments of 0.05, and records the results.

options = optimoptions('ga','MaxGenerations',300,'Display','none');
rng default % for reproducibility
record=[];
for n=0:.05:1
  options = optimoptions(options,'CrossoverFraction',n);
  [x,fval]=ga(@rastriginsfcn,2,[],[],[],[],[],[],[],options);
  record = [record; fval];
end

You can plot the values of fval against the crossover fraction with the following commands:

plot(0:.05:1, record);
xlabel('Crossover Fraction');
ylabel('fval')

The following plot appears.

The plot suggests that you get the best results by setting options.CrossoverFraction to a value somewhere between 0.4 and 0.8.

You can get a smoother plot of fval as a function of the crossover fraction by running ga 20 times and averaging the values of fval for each crossover fraction. The following figure shows the resulting plot.

 Code for Generating the Figure

options = optimoptions('ga','MaxGenerations',300,'Display','none');
rng default % for reproducibility
record=[]; fval = zeros(20,1);
for n=0:.05:1
  options = optimoptions(options,'CrossoverFraction', n);
  for i = 1:20
      [x,fval(i)]=ga(@rastriginsfcn,2,[],[],[],[],[],[],[],options);
  end
  meanf = mean(fval);
  record = [record; meanf];
end

plot(0:.05:1, record);
xlabel('Crossover Fraction');
ylabel('fval')

This plot also suggests the range of best choices for options.CrossoverFraction is 0.4 to 0.8.

Related Topics

• Constrained Minimization Using the Genetic Algorithm
• Coding and Minimizing a Fitness Function Using the Genetic Algorithm