Note: This page has been translated by MathWorks. Click here to see

To view all translated materials including this page, select Country from the country navigator on the bottom of this page.

To view all translated materials including this page, select Country from the country navigator on the bottom of this page.

This example shows how to use a hybrid scheme to optimize a function using the Genetic Algorithm and another optimization method. `ga`

can reach the region near an optimum point relatively quickly, but it can take many function evaluations to achieve convergence. A commonly used technique is to run `ga`

for a small number of generations to get near an optimum point. Then the solution from `ga`

is used as an initial point for another optimization solver that is faster and more efficient for local search.

In this example we will optimize Rosenbrock's function (also known as Dejong's second function):

f(x)= 100*(x(2)-x(1)^2)^2+(1-x(1))^2

This function is notorious in optimization because of the slow convergence most methods exhibit when trying to minimize this function. This function has a unique minimum at the point x* = (1,1) where it has a function value f(x*) = 0.

We can view the code for this fitness function.

```
type dejong2fcn.m
```

function scores = dejong2fcn(pop) %DEJONG2FCN Compute DeJongs second function. %This function is also known as Rosenbrock's function % Copyright 2003-2004 The MathWorks, Inc. scores = zeros(size(pop,1),1); for i = 1:size(pop,1) p = pop(i,:); scores(i) = 100 * (p(1)^2 - p(2)) ^2 + (1 - p(1))^2; end

We use the function `plotobjective`

in the toolbox to plot the function `dejong2fcn`

over the range = [-2 2;-2 2].

plotobjective(@dejong2fcn,[-2 2;-2 2]);

To start, we will use the Genetic Algorithm, `ga`

, alone to find the minimum of Rosenbrock's function. We need to supply `ga`

with a function handle to the fitness function `dejong2fcn.m`

. Also, `ga`

needs to know the how many variables are in the problem, which is two for this function.

FitnessFcn = @dejong2fcn; numberOfVariables = 2;

Some plot functions can be selected to monitor the performance of the solver.

```
options = optimoptions(@ga,'PlotFcn',{@gaplotbestf,@gaplotstopping});
```

We set the random number stream for reproducibility, and run `ga`

with the above inputs.

```
rng('default')
[x,fval] = ga(FitnessFcn,numberOfVariables,[],[],[],[],[],[],[],options)
```

Optimization terminated: average change in the fitness value less than options.FunctionTolerance. x = 0.3454 0.1444 fval = 0.4913

The global optimum is at x* = (1,1). `ga`

found a point near the optimum, but could not get a more accurate answer with the default stopping criteria. By changing the stopping criteria, we might find a more accurate solution, but it may take many more function evaluations to reach x* = (1,1). Instead, we can use a more efficient local search that starts where `ga`

left off. The hybrid function field in `ga`

provides this feature automatically.

We will use a hybrid function to solve the optimization problem, i.e., when `ga`

stops (or you ask it to stop) this hybrid function will start from the final point returned by `ga`

. Our choices are `fminsearch`

, `patternsearch`

, or `fminunc`

. Since this optimization example is smooth, i.e., continuously differentiable, we can use the `fminunc`

function from Optimization Toolbox as our hybrid function. Since `fminunc`

has its own options structure, we provide it as an additional argument when specifying the hybrid function.

fminuncOptions = optimoptions(@fminunc,'Display','iter','Algorithm','quasi-newton'); options = optimoptions(options,'HybridFcn',{@fminunc, fminuncOptions});

Run `ga`

solver again with `fminunc`

as the hybrid function.

[x,fval] = ga(@dejong2fcn,numberOfVariables,[],[],[],[],[],[],[],options)

Optimization terminated: average change in the fitness value less than options.FunctionTolerance. First-order Iteration Func-count f(x) Step-size optimality 0 3 0.664192 28.1 1 12 0.489131 0.000402247 0.373 2 21 0.48383 91 1.22 3 24 0.422036 1 6.7 4 33 0.225633 0.295475 7.36 5 39 0.221682 0.269766 10.1 6 45 0.126376 10 7.96 7 48 0.0839643 1 0.457 8 54 0.0519836 0.5 4.56 9 57 0.0387946 1 5.37 10 60 0.0149721 1 0.85 11 63 0.00959914 1 3.45 12 66 0.0039939 1 0.662 13 69 0.00129755 1 0.348 14 72 0.000288982 1 0.634 15 75 2.29621e-05 1 0.0269 16 78 3.34554e-07 1 0.0139 17 81 5.38696e-10 1 0.000801 18 84 1.72147e-11 1 7.21e-06 Local minimum found. Optimization completed because the size of the gradient is less than the selected value of the optimality tolerance. x = 1.0000 1.0000 fval = 1.7215e-11

The first plot shows the best and mean values of the population in every generation. The best value found by `ga`

when it terminated is also shown in the plot title. When `ga`

terminated, `fminunc`

(the hybrid function) was automatically called with the best point found by `ga`

so far. The solution `x`

and `fval`

is the result of using `ga`

and `fminunc`

together. As shown here, using the hybrid function can improve the accuracy of the solution efficiently.

Was this topic helpful?