# Mixed integer optimisation with genetic algorithm problem

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Calen Walshe on 3 Apr 2012
Commented: Bob Hickish on 17 Jan 2017
Hi,
I'm trying to run setup an optimisation problem using a genetic algorithm with the following specification.
options = gaoptimset(@ga)
[x,fval,exitflag,output,population,scores] = ga(@alpha3_0,6,[0,0,0,1,-1,0],[0],[],[],[25,15,80,45,45,15],[80,15,150,200,200,100],[],[1, 2],options)
The aim is to have integer constraints on parameters 1 and 2 as well as the specified linear inequality.
According to the documentation this should be possible as it states that while equality constraints are not enforced but that the solver shouldn't have a problem with mixed integer problems and inequality constraints.
The results of this so far has been that the optimisation runs without any problems other than that the inequality constraint is completely ignored.
Any help would be greatly appreciated.
Cheers,
Calen
Bob Hickish on 17 Jan 2017
Will update the above question if I find an answer (for other people looking into this).

Seth DeLand on 4 Apr 2012
Hi Calen, The GA solver uses a penalty function when optimizing mixed-integer problems. The penalty function takes into account both the fitness function and the constraint violation. There's some more info here.
So it is possible for infeasible points to get evaluated, and they return a high value for the penalty function. You could try adjusting the PenaltyFactor and InitialPenalty values to increase the penalty applied to points that violate the constraint. This won't stop all infeasible points from being evaluated, but it will make their penalty value worse which hopefully results in the solver moving away from those points faster.
suvrat on 26 Jan 2014
i am facing the same problem could you please tell me how you work on this problem.

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