Optimization with Genetic algorithm

I have to find the global minimum of a function which has 11 independent variables using ga

[X,FVAL,EXITFLAG] = ga(@FitnessFunc, 11, [],[],[],[],lb, ub, [],INTCON, options);

Some of these variables are integer. This function has a lot of local minima, and I am not able to locate the global one. I tried to increase the Population Size and Generation but the resulting solutions are always trapped in regions that are worse than the optimal one ( I know what is the solution of the problem because i found it in literature).

Can you suggest me how to set the other options of the Genetic Algorithm (Crossover, Mutation, Selection...) in order to resolve the problem ( I think I have to increase the diversity in the population).

I read about a particular technique called "niche technique" or "niching algorithm"; it uses knowledge gained during one usage of GA to avoid re-searching, on subsequent GA usage, regions of problem space where solution have already been found. Consequently, the likelihood of discovering a new solution on each iteration is increased. Do you know if a Matlab script of this technique is avaible, or if it is already implemented in Matlab ga toolbox?