Help wanted: solution to very simple problem? about evolutionary.jl HOT 1 CLOSED

The fitness function is ill-defined. There is no variation in the resulting value it always +1 (the chance of getting -1 is close to 0 in the real domain).

So after the couple iterations the difference between previous and current fitness values is 0 which is less then the tolerance threshold, so the algorithm going to stop. There are additional 10 iterations of the optimization on successful tolerance condition, controlled by successive_f_tol option.

You may want to try the following fitness function fit(x) = abs(4.0 - sum(x))

julia> fit(x) = abs(4.0 - sum(x))
fit (generic function with 1 method)

julia> Evolutionary.optimize(fit,lowerbound,upperbound,indiv,CMAES(),Evolutionary.Options(iterations = 1000))

 * Status: success

 * Candidate solution
    Minimizer:  [2.2801910167618544, 1.719808983238146]
    Minimum:    0.0
    Iterations: 478

 * Found with
    Algorithm: (15,30)-CMA-ES

from evolutionary.jl.