Resume ga
By default, ga creates a new initial population each time you run it. However, you might get better results by using the final population from a previous run as the initial population for a new run. To do so, you must have saved the final population from the previous run by calling ga with the syntax
[x,fval,exitflag,output,final_pop] = ga(@fitnessfcn,nvars);
The last output argument is the final population. To run ga using final_pop as the initial population, enter
options = optimoptions('ga','InitialPop',final_pop); [x,fval,exitflag,output,final_pop2] = ... ga(@fitnessfcn,nvars,[],[],[],[],[],[],[],options);
You can then use final_pop2, the final population from the second run, as the initial population for a third run.
For example, minimize Ackley's function, a function of two variables that is available when you run this example.
rng(100) % For reproducibiliity
[x,fval,exitflag,output,final_pop] = ga(@ackleyfcn,2);ga stopped because the average change in the fitness value is less than options.FunctionTolerance.
Examine the best function value.
disp(fval)
3.5527
Try to get a better solution by running ga from the final population.
options = optimoptions('ga','InitialPopulationMatrix',final_pop); [x,fval2,exitflag2,output2,final_pop2] = ... ga(@ackleyfcn,2,[],[],[],[],[],[],[],options);
ga stopped because the average change in the fitness value is less than options.FunctionTolerance.
disp(fval2)
2.9886
The fitness function value improves significantly.
Try once again to improve the solution.
options.InitialPopulationMatrix = final_pop2; [x,fval3,exitflag3,output3,final_pop3] = ... ga(@ackleyfcn,2,[],[],[],[],[],[],[],options);
ga stopped because the average change in the fitness value is less than options.FunctionTolerance.
disp(fval3)
2.9846
This time the improvement is insignificant.
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