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

Plot the values of fval against the crossover fraction.

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

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.

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.

See Also

ga

Related Topics

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