parent population

[FrancescoCavarretta]

I am using BluePyOpt to optimize a model implemented in NEURON.

In the optimization, I choose a parent population generated a-priori.

The average fitness of the individuals worsens over the generations rather than improving. Why does it happen? What is wrong?

[AurelienJaquier]

Hi @FrancescoCavarretta ! What is the optimiser that you use in BluePyOpt? Is it IBEADEAPOptimisation or DEAPOptimisationCMA? Also could you share a checkpoint with us so that we can inspect what is happening?

[FrancescoCavarretta]

Hi Aurelien:
Yes, I can share the checkpoint. I could upload the file into my onedrive and share the link with you via e-mail, if you provide you it. If it does not work for you, let me know your preference.

[AurelienJaquier]

Sure! You can send the link to me at [email protected]

[AurelienJaquier]

Thank you @FrancescoCavarretta . First of all, I have checked the evolution of the best individual over the optimisation, and we can see that it tends to go towards lower scores, so the optimisation is working.
As for why the average score is going up, I think it proabably has to do with the initial population you gave to BluePyOpt. As I don't have your scoring function, I could not test my hypothesis, but I think that the following is happening:
I think your initial population is located in a region wit a good average score. When BluePyOpt tries to optimise the population, it will search the parameter space, thus going partially outside of this region, increasing the number of individuals with higher scores, thus increasing the mean score. And then after some time, the mean score starts going down. Even if the mean score is not going back to the value it had at the start very fast, the best individual has improved.