function rosenbrock(x::AbstractVector{T}) where T
s=(1.0 - x[1])^2
for i in 1:(length(x)-1)
s+=100.0 * (x[i+1] - x[i]^2)^2
end
return s
end
optimize(rosenbrock,[0.3,0.6],1.0,sNES) # separable natural es.
(sol = [0.9999902815083116, 0.9999805401026993], cost = 9.450201922031972e-11)
optimize(rosenbrock,[0.3,0.6],1.0,xNES) # exponential natural es.
(sol = [0.9999999934969991, 0.9999999871800216], cost = 4.574949214506023e-17)
for further info in Julia type ?optimize.
Tips:
Use xNES for hard problems with strongly correlated variables
Use sNES for high dimensional problems that exhibit many local minima
Use sNES for problems with mostly separable variables
Future Plans:
Implementing other strongly performing variants
Parallelization
if there is any interest in having this package registered, let me know!
you can now install this package by typing ]add NaturalES in the Julia REPL.
i’d say in some cases it can compare very favorably
using BenchmarkTools
using BlackBoxOptim
using NaturalES
function rosenbrock(x::AbstractVector{T}) where T
s=(1.0 - x[1])^2
for i in 1:(length(x)-1)
s+=100.0 * (x[i+1] - x[i]^2)^2
end
return s
end
function bb_task()
best_candidate(bboptimize(rosenbrock; SearchRange = (-5.0, 5.0), NumDimensions = 2, Method = :separable_nes,TraceMode=:silent))
end
function nates_task()
optimize(rosenbrock,[0.0,0.0],2.0,sNES).sol
end
@btime bb_task() #19.940 ms (276040 allocations: 14.91 MiB)
@btime nates_task() #652.101 μs (3867 allocations: 61.84 KiB)
print(bb_task()) #[0.9876181286752018, 0.975347963147114]
print(nates_task()) #[0.9999963410120233, 0.9999926866427169]
using LinearAlgebra
print(norm(bb_task().-[1,1])/norm(nates_task().-[1,1])) # ~ 10^3
much less time,much less allocations, for a much better solution
in this case my package is ~ 31 times faster and it reaches a solution three order of magnitude more accurate
the argument is still valid also for xNES
function bb_task()
best_candidate(bboptimize(rosenbrock; SearchRange = (-5.0, 5.0), NumDimensions = 2, Method = :xnes,TraceMode=:silent))
end
function nates_task()
optimize(rosenbrock,[0.0,0.0],2.0,xNES).sol
end
@btime bb_task() #126.951 ms (326961 allocations: 26.50 MiB)
@btime nates_task() #175.700 μs (2743 allocations: 233.63 KiB)
Note that the performance that people care about usually is the probability of finding the global minima in function of the number of function evaluations, not really the time the algorithm needs to run (since the bottleneck if often evaluating the error function).
I have a small framework to do that kind of benchmarks if you are interested.
