I am trying to use the ga function to optimize a function, but am noticing a strange behavior. My function has 5 variables that are bound to between 0 and -10 and there is a equality constraint that in the form of c(x) -1 = 0. I am using the following options:
options = optimoptions('ga','UseVectorized',true,'PopulationSize',3000,'InitialPopulationMatrix',intpop2,'MaxGenerations',50,...
'FunctionTolerance',1e-4,'ConstraintTolerance',1e-4,'Display','iter');
where the initial poptulaiton is a matrix of all combinations of the five variable (equally spaced along a grid between 0 and -10). The problem I am having is that I frequently notice that after several generation the ga function returns worse results. When exaiminging the output of each iteration, an acceptable answer seems to be found after 5 or so iterations; however the function continues and the results become worse. I have tried to change the function tolerance and constraint tolerance but the results are largely the same or become inconsistent (outside of the normal variations when using optimaztion functions). Below is an example of the output I frequently get.
Generation Func-count Best f(x) Max Constraint Stall Generations
1 162500 0.00106595 0.01563 0
2 314182 0.000272166 0.01934 0
3 473557 0.00190035 0.01894 0
4 632932 0.000113225 0.01067 0
5 792307 4.30226e-05 0.005452 0
6 943989 0.000232475 0.001222 0
7 1103364 1.50892e-06 0.001525 0
8 1262739 1.50892e-06 0.001525 1
9 1422114 1.50892e-06 0.001525 2
10 1581489 1.50892e-06 0.001525 3
11 1740864 1.50892e-06 0.001525 4
12 1900239 6.42236e-05 0.002838 0
14 2218989 3.37523 14.95 0
18 2856489 3.26691 0.9944 0
20 3175239 5.10486 0.5483 0
