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One of the most important factors that determines the performance of the genetic
algorithm performs is the *diversity* of the population. If the
average distance between individuals is large, the diversity is high; if the average
distance is small, the diversity is low. Getting the right amount of diversity is a
matter of trial and error. If the diversity is too high or too low, the genetic
algorithm might not perform well.

This section explains how to control diversity by setting the initial range of the population. Setting the Amount of Mutation describes how the amount of mutation affects diversity.

This section also explains how to set the population size.

By default, `ga`

creates a random initial population using a creation function. You can specify the range of the vectors in the initial population in the `InitialPopulationRange`

option.

**Note:** The initial range restricts the range of the points in the initial population by specifying the lower and upper bounds. Subsequent generations can contain points whose entries do not lie in the initial range. Set upper and lower bounds for all generations using the `lb`

and `ub`

input arguments.

If you know approximately where the solution to a problem lies, specify the initial range so that it contains your guess for the solution. However, the genetic algorithm can find the solution even if it does not lie in the initial range, if the population has enough diversity.

This example shows how the initial range affects the performance of the genetic algorithm. The example uses Rastrigin's function, described in Minimize Rastrigin's Function. The minimum value of the function is 0, which occurs at the origin.

rng(1) % For reproducibility fun = @rastriginsfcn; nvar = 2; options = optimoptions('ga','PlotFcn',{'gaplotbestf','gaplotdistance'},... 'InitialPopulationRange',[1;1.1]); [x,fval] = ga(fun,nvar,[],[],[],[],[],[],[],options)

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

`x = `*1×2*
0.9942 0.9950

fval = 1.9900

The upper plot, which displays the best fitness at each generation, shows little progress in lowering the fitness value. The lower plot shows the average distance between individuals at each generation, which is a good measure of the diversity of a population. For this setting of initial range, there is too little diversity for the algorithm to make progress.

Next, try setting the `InitialPopulationRange`

to [1;100]. This time the results are more variable. The current random number setting causes a fairly typical result.

options.InitialPopulationRange = [1;100]; [x,fval] = ga(fun,nvar,[],[],[],[],[],[],[],options)

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

`x = `*1×2*
0.9344 -1.0792

fval = 4.0889

This time, the genetic algorithm makes progress, but because the average distance between individuals is so large, the best individuals are far from the optimal solution.

Now set the `InitialPopulationRange`

to [1;2]. This setting is well-suited to the problem.

options.InitialPopulationRange = [1;2]; [x,fval] = ga(fun,nvar,[],[],[],[],[],[],[],options)

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

`x = `*1×2*
0.0013 0.0020

fval = 0.0011

The suitable diversity usually causes `ga`

to return a better result than in the previous two cases.

`ga`

This example shows how `@gacreationlinearfeasible`

, the default creation function for linearly constrained problems, creates a population for `ga`

. The population is well-dispersed, and is biased to lie on the constraint boundaries. The example uses a custom plot function.

**Fitness Function**

The fitness function is `lincontest6`

, included with your software. This is a quadratic function of two variables:

**Custom Plot Function**

Save the following code to a file on your MATLAB® path named `gaplotshowpopulation2`

.

function state = gaplotshowpopulation2(~,state,flag,fcn) %gaplotshowpopulation2 Plots the population and linear constraints in 2-d. % STATE = gaplotshowpopulation2(OPTIONS,STATE,FLAG) plots the population % in two dimensions. % % Example: % fun = @lincontest6; % options = gaoptimset('PlotFcn',{{@gaplotshowpopulation2,fun}}); % [x,fval,exitflag] = ga(fun,2,A,b,[],[],lb,[],[],options); % This plot function works in 2-d only if size(state.Population,2) > 2 return; end if nargin < 4 fcn = []; end % Dimensions to plot dimensionsToPlot = [1 2]; switch flag % Plot initialization case 'init' pop = state.Population(:,dimensionsToPlot); plotHandle = plot(pop(:,1),pop(:,2),'*'); set(plotHandle,'Tag','gaplotshowpopulation2') title('Population plot in two dimension','interp','none') xlabelStr = sprintf('%s %s','Variable ', num2str(dimensionsToPlot(1))); ylabelStr = sprintf('%s %s','Variable ', num2str(dimensionsToPlot(2))); xlabel(xlabelStr,'interp','none'); ylabel(ylabelStr,'interp','none'); hold on; % plot the inequalities plot([0 1.5],[2 0.5],'m-.') % x1 + x2 <= 2 plot([0 1.5],[1 3.5/2],'m-.'); % -x1 + 2*x2 <= 2 plot([0 1.5],[3 0],'m-.'); % 2*x1 + x2 <= 3 % plot lower bounds plot([0 0], [0 2],'m-.'); % lb = [ 0 0]; plot([0 1.5], [0 0],'m-.'); % lb = [ 0 0]; set(gca,'xlim',[-0.7,2.2]) set(gca,'ylim',[-0.7,2.7]) axx = gcf; % Contour plot the objective function if ~isempty(fcn) range = [-0.5,2;-0.5,2]; pts = 100; span = diff(range')/(pts - 1); x = range(1,1): span(1) : range(1,2); y = range(2,1): span(2) : range(2,2); pop = zeros(pts * pts,2); values = zeros(pts,1); k = 1; for i = 1:pts for j = 1:pts pop(k,:) = [x(i),y(j)]; values(k) = fcn(pop(k,:)); k = k + 1; end end values = reshape(values,pts,pts); contour(x,y,values); colorbar end % Show the initial population ax = gca; fig = figure; copyobj(ax,fig);colorbar % Pause for three seconds to view the initial plot, then resume figure(axx) pause(3); case 'iter' pop = state.Population(:,dimensionsToPlot); plotHandle = findobj(get(gca,'Children'),'Tag','gaplotshowpopulation2'); set(plotHandle,'Xdata',pop(:,1),'Ydata',pop(:,2)); end

The custom plot function plots the lines representing the linear inequalities and bound constraints, plots level curves of the fitness function, and plots the population as it evolves. This plot function expects to have not only the usual inputs `(options,state,flag)`

, but also a function handle to the fitness function, `@lincontest6`

in this example. To generate level curves, the custom plot function needs the fitness function.

**Problem Constraints**

Include bounds and linear constraints.

A = [1,1;-1,2;2,1]; b = [2;2;3]; lb = zeros(2,1);

**Options to Include Plot Function**

Set options to include the plot function when `ga`

runs.

options = optimoptions('ga','PlotFcns',... {{@gaplotshowpopulation2,@lincontest6}});

**Run Problem and Observe Population**

The initial population, in the first plot, has many members on the linear constraint boundaries. The population is reasonably well-dispersed.

rng default % for reproducibility [x,fval] = ga(@lincontest6,2,A,b,[],[],lb,[],[],options);

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

`ga`

converges quickly to a single point, the solution.

The **Population size** field in **Population**
options determines the size of the population at each generation. Increasing the
population size enables the genetic algorithm to search more points and thereby
obtain a better result. However, the larger the population size, the longer the
genetic algorithm takes to compute each generation.

**Note**

You should set **Population size** to be at least the value
of **Number of variables**, so that the individuals in each
population span the space being searched.

You can experiment with different settings for **Population
size** that return good results without taking a prohibitive amount of
time to run.