## How are the starting points for surrogate optimization chosen?

I have a question regarding the "starting points" or the random points chosen to construct the surrogate. Is it possible to access the function that determines how the surrogate selects its random points?

I am testing different algorithms in MATLAB for a global optimization problem, and I always perform 50 different trials of 100 iterations each. The surrogate algorithm consistently produces very similar starting points and performs exceptionally well with them. The documentation describes this process as a pseudorandom sequence (https://en.wikipedia.org/wiki/Low-discrepancy_sequence), but in my opinion, this does not explain the similarity of the points shown on the cost function graph, especially since no random seed is set.

The graph displays the mean, minimum, and maximum of the cost function for the surrogate algorithm with random starts (over the initial X condition in the options of the surrogate) called Surrogate RNG and the surrogate algorithm. It is interesting to note how close the minimum and maximum of the cost function of the surrogate are.

Is it possible to have more options for surrogate optimization (such as those available in https://github.com/Piiloblondie/MATSuMoTo)? Additionally, is it possible to access the function for determining the starting points so that I can try it with other algorithms?

What could be the reason for the starting points to be so close together even when no random seed is chosen?

For additional context, I use 8-13 different variables, all of which are doubles. I also use one linear constraint that can be implemented in the function.I have a question regarding the "starting points" or the random points chosen to construct the surrogate. Is it possible to access the function that determines how the surrogate selects its random points?

I am testing different algorithms in MATLAB for a global optimization problem, and I always perform 50 different trials of 100 iterations each. The surrogate algorithm consistently produces very similar starting points and performs exceptionally well with them. The documentation describes this process as a pseudorandom sequence (https://en.wikipedia.org/wiki/Low-discrepancy_sequence), but in my opinion, this does not explain the similarity of the points shown on the cost function graph, especially since no random seed is set.

The graph displays the mean, minimum, and maximum of the cost function for the surrogate algorithm with random starts (over the initial X condition in the options of the surrogate) called Surrogate RNG and the surrogate algorithm. It is interesting to note how close the minimum and maximum of the cost function of the surrogate are.

Is it possible to have more options for surrogate optimization (such as those available in https://github.com/Piiloblondie/MATSuMoTo)? Additionally, is it possible to access the function for determining the starting points so that I can try it with other algorithms?

What could be the reason for the starting points to be so close together even when no random seed is chosen?

For additional context, I use 8-13 different variables, all of which are doubles. I also use one linear constraint that can be implemented in the function. I have a question regarding the "starting points" or the random points chosen to construct the surrogate. Is it possible to access the function that determines how the surrogate selects its random points?

I am testing different algorithms in MATLAB for a global optimization problem, and I always perform 50 different trials of 100 iterations each. The surrogate algorithm consistently produces very similar starting points and performs exceptionally well with them. The documentation describes this process as a pseudorandom sequence (https://en.wikipedia.org/wiki/Low-discrepancy_sequence), but in my opinion, this does not explain the similarity of the points shown on the cost function graph, especially since no random seed is set.

The graph displays the mean, minimum, and maximum of the cost function for the surrogate algorithm with random starts (over the initial X condition in the options of the surrogate) called Surrogate RNG and the surrogate algorithm. It is interesting to note how close the minimum and maximum of the cost function of the surrogate are.

Is it possible to have more options for surrogate optimization (such as those available in https://github.com/Piiloblondie/MATSuMoTo)? Additionally, is it possible to access the function for determining the starting points so that I can try it with other algorithms?

What could be the reason for the starting points to be so close together even when no random seed is chosen?

For additional context, I use 8-13 different variables, all of which are doubles. I also use one linear constraint that can be implemented in the function. surrogate, global optimization toolbox MATLAB Answers — New Questions