Constrained Optimization for Genetic Algorithms [DEMO Included] 

I love your videos, you make anything easier

please do more videos, you are a gem! keep up the good work

Thanks for the video man. I am new to optimization so please forgive me if my question seems stupid but looking at the function 2, if the values of x1 and x2 are 0.1 and 5 respectively, the value of f_2(x) will be 60, however your plot says 10. Am I missing something?

Great video man, really helped to clear up some things I was struggling with. I'm currently working on a function approximation + multi-objective optimization project. I have 2 unknown functions that I can request a limited (2000) number of points (X,Y,Z inputs) and create a surrogate model for both. I then have to find the pareto front for both surrogate models. Due to 2000 data point limit, I'm curious if a genetic algorithm like NSGA-II could help me request meaningful data points to better minimize the functions within a unit hypercube of dimensions [-5,5]^3. Do you have any advice on how to approach this or if a genetic algorithm is a good approach to this problem?

If I'm not mistaken, your 1st example is not what you described as functions: when x1, x2=0, F1=0 and F2 =-8. Both F1 and F2 are ranging form -8 to 0....

Hey brother, is there any repository for your code? I am in dire need of a solution for a similar problem.

can we use this approach for hard constraints?

could you explain this algorithm An Evolutionary Multi-Objective Crowding Algorithm (EMOCA) because i can't get his code

brav000!!

hello how are you my dear thats great video i have problem can you help me

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