These scritps implement the version of the Genetic Algorithm decribed in
"Control predictivo basado en modelos mediante técnica de optimización heurística. Aplicación a procesos no lineales y multivariables. F. Xavier Blasco Ferragud. PhD Tesis 1999 (in Spanish). Editorial UPV. ISBN 84-699-5429-6.
It is an easy to use GA and basic instructions are supplied.
Available at: http://hdl.handle.net/10251/15995
Answer to Mehdi Elkaddouri
I can't reproduce the error. I suppose your are running example.m, but everything works well when I run it. Your comment suggest me you you don't have executed the line 1 to 4 of the example or you have delete the variable gaDat before executing line 5.
Undefined function or variable 'gaDat'.
Error in ga (line 5)
gaDat=ga(gaDat) how could i pass this error ?
hello Xavier thank you so much for the code, my problem is to maximize the power of PV system using genetic algorithm.thank you so much
Answer to Lydia Hamis.
You have to build a cost function for your problem. There is a short tutorial where you can see examples of use.
My study regarding image reconstruction using constrained least squares filter. In order to get better result, i would like to implement GA. How do i implement this code to the current results?
Thanks for the code, quick conversion from Matlab solver GA to yours
Answer to Jaouadi Zouhour.
No heuristic algorithm can guarantee to have found the global optimum. In the current version of the algorithm the stop is done with a fixed number of iterations, but the user can add his own criterion of stop in the function gaiteration.m. This function is executed at each iteration of the algorithm.
For instances, you could add:
Change yourStopCriterionIsSatisfied by your own condition.
Thank you for the code.
I have a question, While applying the ga alg. my optimal function converges to a fix value starting from an iteration (iteration: 50). does it correspond to the optimal point? is it possible to stop the iterations and assume it as the minimum ?
New SLM scheme to reduce the PAPR of OFDM signals using
a genetic algorithm
Correcting the order in the way each the gaiteration is performed.
Bug fixed. Improved code efficiency.
Create scripts with code, output, and formatted text in a single executable document.