# Hybrid Scheme in the Genetic Algorithm

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

can quickly reach a neighborhood of a local minimum, but it can require many function evaluations to achieve convergence. To speed the solution process, first run `ga`

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

as the initial point for another optimization solver to perform a faster and more efficient local search.

### Rosenbrock's Function

This example uses Rosenbrock's function (also known as Dejong's second function) as the fitness function:

$$f(x)=100(x(2)-x(1{)}^{2}{)}^{2}+(1-x(1){)}^{2}$$.

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

The code for Rosenbrock's function is in the `dejong2fcn`

file.

`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

Plot Rosenbrock's function over the range –2 <= x(1) <= 2; –2 <= x(2) <=2.

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

### Genetic Algorithm Solution

First, use `ga`

alone to find the minimum of Rosenbrock's function.

FitnessFcn = @dejong2fcn; numberOfVariables = 2;

Include plot functions to monitor the optimization process.

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

Set the random number stream for reproducibility, and run `ga`

using the options.

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

ga stopped because the average change in the fitness value is less than options.FunctionTolerance.

`x = `*1×2*
0.3454 0.1444

fval = 0.4913

Using the default stopping criteria, `ga`

does not provide a very accurate solution. You can change the stopping criteria to try to find a more accurate solution, but `ga`

requires many function evaluations to approach the global optimum x* = (1,1).

Instead, perform a more efficient local search that starts where `ga`

stops by using the hybrid function option in `ga`

.

### Adding a Hybrid Function

A hybrid function begins from the point where `ga`

stops. Hybrid function choices are `fminsearch`

, `patternsearch`

, or `fminunc`

. Because this optimization example is smooth and unconstrained, use `fminunc`

as the hybrid function. Provide `fminunc`

with plot options as an additional argument when specifying the hybrid function.

fminuncOptions = optimoptions(@fminunc,'PlotFcn',{'optimplotfval','optimplotx'}); options = optimoptions(options,'HybridFcn',{@fminunc, fminuncOptions});

Run `ga`

again with `fminunc`

as the hybrid function.

[x,fval,exitflag,output] = ga(FitnessFcn,numberOfVariables,[],[],[],[],[],[],[],options)

ga stopped because the average change in the fitness value is less than options.FunctionTolerance.

Local minimum found. Optimization completed because the size of the gradient is less than the value of the optimality tolerance.

`x = `*1×2*
1.0000 1.0000

fval = 1.7215e-11

exitflag = 1

`output = `*struct with fields:*
problemtype: 'unconstrained'
rngstate: [1x1 struct]
generations: 51
funccount: 2534
message: 'ga stopped because the average change in the fitness value is less than options.FunctionTolerance....'
maxconstraint: []
hybridflag: 1

The `ga`

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

when it stops. The hybrid function `fminunc`

starts from the best point found by `ga`

. The `fminunc`

plot shows the solution `x`

and `fval`

, which result from using `ga`

and `fminunc`

together. In this case, using a hybrid function improves the accuracy and efficiency of the solution. The `output.hybridflag`

field shows that `fminunc`

stops with exit flag 1, indicating that `x`

is a true local minimum.