facing issue in using scaling_of_variables cma es for my problem.

[pramodiisc]

I am having a function f(x1,x2,x3,x4,x5,x6) that has to be minimised for the variable x1,x2,x3,x4,x5,x6. The range for the variables or the search domain is
x1 (300 to 1500),
x2 (0-10),
x3 (300 to 1500),
x4(0-10) ,
x5 (500 to 20000),
x6 (0-200)
which means the algo should search for variable falling in this range.

I am getting the function value from a simulator.

help(cma.fitness_transformations.ScaleCoordinates) is not clear to me how can I use it for my problem.

sigma0
scalar, initial standard deviation in each coordinate. sigma0 should be about 1/4th of the search domain width (where the optimum is to be expected). The variables in objective_function should be scaled such that they presumably have similar sensitivity. See also option scaling_of_variables.

if I am not usingcaleCoordinates I can not use sigma 0 bigger than 10 (x4(0-10) ) which is making the algo not searching in wider domain.

Can you please share or show any simpler example.

[nikohansen]

One typical approach is to scale the variables such that the above domain is sent to the interval [0, 1] in each variable (e.g. with ScaleCoordinates, for the first variable we would put multipliers = 1200, zero = -300/1200 such that 0 -> multipliers * (0 - zero) = 300 and 1 -> multipliers * (1 - zero) = 1500), however see also these practical hints. After that, the search should be bounded to the domain [0,1]. Example code to pass domain boundaries is given in the basic use cases notebook under Options and Bound Constraints.

This example suggests though that ScaleCoordinates could be implemented with a more user friendly interface: it should better return multipliers * x + zero, or it could accept lower and upper coordinate values to which 0 and 1 should map to, respectively, which would then be lower = zero and upper = zero + multipliers or equivalently upper - lower = multipliers.

[nikohansen]

Addressed with commit 5ac2a14