Evolutionary algorithm: basic components and the main loop

[@preetamsharma5220]

Question: what if I am interested in finding the local minima on that 3d plot (multi dimensional landscape). How can I find the local minima?

[@Maciej-Komosinski]

You use local search algorithms ("greedy" or "steepest") as explained in earlier lectures. Did you watch these earlier videos?

[@preetamsharma5220]

@@Maciej-Komosinski yes I have watched as greedy algorithm works on a lot of Calculas calculation and we have to choose a arbitrary point for solving it. Do we really care about solving or thinking to find local minima manually?

[@Maciej-Komosinski]

@@preetamsharma5220 If you have a continuous function, then you can indeed use gradient methods. But here we speak about a more general approach to optimization where the space of solutions does not have to be continuous. Metaheuristic algorithms, as discussed in this class, will also work for combinatorial optimization problems where calculus will not help. No, we definitely will not look for local minima manually (though I am not sure what you mean by "manually").

[@preetamsharma5220]

@@Maciej-Komosinski Now I am clear, Manually means without help of programming. Thanks for response

[@preetamsharma5220]

If we use the different fitness functions, increasing the rate of mutations to maintain a diverse population of solution what is the probability that if we are scarfing short term fitness to obtain longer term fitness. Is there any chance that it can't convert into longer term fitness as the "no free lunch theorem proves that there is no general solution to this problem.

[@Maciej-Komosinski]

The more intense the mutation, the more diversification we introduce in the population thus preventing (or delaying) convergence. Larger mutations increase exploration and decrease exploitation.

[@preetamsharma5220]

@@Maciej-Komosinski thanks