Mutation stage of genetic algorithm in Matlab

I'm optimizing an image reconstruction algorithm using genetic algorithm in Matlab.I did crossover on two population and generate two offsprings without using 'ga' toolkit in matlab. So presently I have two 1*n matrices with integer values ranging from 0-255(They are two images in row major order).for example

population_1 = [1 2 3 4 5 6 7 8 9 10]
population_2 = [10 20 30 40 50 60 70 80 90 100]

And I did single point ordered cross over and got offsprings as

Off_1 =  1     2     3     4     5    60    70    80    90   100
Off_2 =  10    20    30    40    50     6     7     8     9    10

Next I need to do mutation with probability rate of 0.02.I used 'gaoptimset' here and coded as follows.

 mutated_child = gaoptimset('MutationFcn', {@mutationuniform, .02})

and I printed the result.It gives a structure like this without any values.

mutated_child = 

    PopulationType: []
      PopInitRange: []
    PopulationSize: []
        EliteCount: []
 CrossoverFraction: []
    ParetoFraction: []
MigrationDirection: []
 MigrationInterval: []
 MigrationFraction: []
       Generations: []
         TimeLimit: []
      FitnessLimit: []
     StallGenLimit: []
    StallTimeLimit: []
            TolFun: []
            TolCon: []
 InitialPopulation: []
     InitialScores: []
    InitialPenalty: []
     PenaltyFactor: []
      PlotInterval: []
       CreationFcn: []
 FitnessScalingFcn: []
      SelectionFcn: []
      CrossoverFcn: []
       MutationFcn: {[@mutationuniform]  [0.0200]}
DistanceMeasureFcn: []
         HybridFcn: []
           Display: []
          PlotFcns: []
        OutputFcns: []
        Vectorized: []
       UseParallel: []

Can anyone please help me to perform mutation on crossovered childs(Off_1 and Off_2)?Thanks in advance.

I don't know anything about the GA toolbox. But without it you could do something like:

% for offspring 1:

p_m = 0.02;
for i = 1:length(Off_1)
    if rand(1) < p_m
        Off_1(i) = randi([0,255],1);
    end
end

You should do the same thing with offspring no. 2