create custom plot function -凯发k8网页登录

about custom plot functions

if none of the plot functions that come with the software is suitable for the output you want to plot, you can write your own custom plot function, which the genetic algorithm calls at each generation to create the plot. this example shows how to create a plot function that displays the change in the best fitness value from the previous generation to the current generation.

creating the custom plot function

to create the plot function for this example, copy and paste the following code into a new file in the matlab^® editor.

function state = gaplotchange(options, state, flag)
% gaplotchange plots the logarithmic change in the best score from the 
% previous generation.
%   
persistent last_best % best score in the previous generation
if(strcmp(flag,'init')) % set up the plot
    xlim([1,options.maxgenerations]);
    axx = gca;
    axx.yscale = 'log';
    hold on;
    xlabel generation
    title('log absolute change in best fitness value')
end
best = min(state.score); % best score in the current generation
if state.generation == 0 % set last_best to best.
    last_best = best;
else
	change = last_best - best; % change in best score 
	last_best = best;
    if change > 0 % plot only when the fitness improves
        plot(state.generation,change,'xr');
    end
end

save the file as gaplotchange.m in a folder on the matlab path.

using the custom plot function

to use the custom plot function, include it in the options.

rng(100) % for reproducibility
options = optimoptions('ga','plotfcn',{@gaplotbestf,@gaplotchange});
[x,fval] = ga(@rastriginsfcn,2,[],[],[],[],[],[],[],options)

optimization terminated: maximum number of generations exceeded.
x =
   -0.0003    0.0014
fval =
   4.2189e-04

the plot shows only changes that are greater than 0, which are improvements in best fitness. the logarithmic scale enables you to see small changes in the best fitness function that the upper plot does not reveal.

how the plot function works

the plot function uses information contained in the following structures, which the genetic algorithm passes to the function as input arguments:

the most important lines of the plot function are the following:

the code for gaplotchange contains many of the same elements as the code for gaplotbestf, the function that creates the best fitness plot.