miesmuschel provides evolutionary black box optimization algorithms, building on the bbotk package. miesmuschel offers both ready-to-use optimization algorithms, as well as their fundamental building blocks that can be used to manually construct specialized optimization loops.
(also giving a hint on how to pronounce "miesmuschel")
The R software package miesmuschel
Offers opt-algorithms, so crucial
bbotk is its base, it's a powerful tool
For optimization, it's truly no fool
Ready-made or D-I-Y, the choice is yours
With miesmuschel, your options are wide open doors
So when you do optimize, just give it a try
With miesmuschel, surely, success is nigh!
Although miesmuschel is currently still evolving, it can already be used for optimization. All exported functions are thoroughly documented.
Install the github version, using remotes:
remotes::install_github("mlr-org/miesmuschel")library("bbotk")
library("paradox")
lgr::threshold("warn")
objective <- ObjectiveRFun$new(
fun = function(xs) {
z <- exp(-xs$x^2 - xs$y^2) + 2 * exp(-(2 - xs$x)^2 - (2 - xs$y)^2)
list(Obj = z)
},
domain = ps(x = p_dbl(-2, 4), y = p_dbl(-2, 4)),
codomain = ps(Obj = p_dbl(tags = "maximize"))
)This is the recommended way of using miesmuschel.
# Get a new OptimInstance
oi <- OptimInstanceSingleCrit$new(objective,
terminator = trm("evals", n_evals = 100)
)
library("miesmuschel")
# Get operators
op.m <- mut("gauss", sdev = 0.1)
op.r <- rec("xounif", p = .3)
op.parent <- sel("random")
op.survival <- sel("best")
# Create OptimizerMies object
mies <- opt("mies", mutator = op.m, recombinator = op.r,
parent_selector = op.parent, survival_selector = op.survival,
mu = 3, lambda = 2)
# mies$optimize performs MIES optimization and returns the optimum
mies$optimize(oi)
#> x y x_domain Obj
#> 1: 1.935055 1.973867 <list[2]> 1.990703This gives more flexibility when designing ES algorithms, but it is also more verbose and error-prone.
# Get a new OptimInstance
oi <- OptimInstanceSingleCrit$new(objective,
terminator = trm("evals", n_evals = 100)
)
library("miesmuschel")
# Get operators
op.m <- mut("gauss", sdev = 0.1)
op.r <- rec("xounif", p = .3)
op.parent <- sel("random")
op.survival <- sel("best")
# Prime operators
mies_prime_operators(list(op.m), list(op.r), list(op.parent, op.survival),
search_space = oi$search_space)
# Sample first generation
mies_init_population(oi, 3)
# This is the first generation
oi$archive$data[, .(x, y, Obj, dob, eol)]
#> x y Obj dob eol
#> 1: 3.8516312 1.2386550 0.03633278 1 NA
#> 2: -1.6478480 -0.9080712 0.02901343 1 NA
#> 3: -0.4215587 0.8250017 0.42529339 1 NA
# Select parents, recombine, mutate
offspring <- mies_generate_offspring(oi, 2, op.parent, op.m, op.r)
# This is the first offspring population
offspring
#> x y
#> 1: 2.762783 -0.24885684
#> 2: -1.439780 -0.05699817
# Evaluate offspring (and append to oi archive)
mies_evaluate_offspring(oi, offspring)
# State of the archive now: Second generation has `dob` == 2
oi$archive$data[, .(x, y, Obj, dob, eol)]
#> x y Obj dob eol
#> 1: 3.8516312 1.23865501 0.036332776 1 NA
#> 2: -1.6478480 -0.90807120 0.029013430 1 NA
#> 3: -0.4215587 0.82500173 0.425293387 1 NA
#> 4: 2.7627829 -0.24885684 0.007566604 2 NA
#> 5: -1.4397798 -0.05699817 0.125404228 2 NA
# Selecto for survival
mies_survival_plus(oi, 3, op.survival)
# Survivors have `eol` NA, two individuals 'died' in generation 2
oi$archive$data[, .(x, y, Obj, dob, eol)]
#> x y Obj dob eol
#> 1: 3.8516312 1.23865501 0.036332776 1 NA
#> 2: -1.6478480 -0.90807120 0.029013430 1 2
#> 3: -0.4215587 0.82500173 0.425293387 1 NA
#> 4: 2.7627829 -0.24885684 0.007566604 2 2
#> 5: -1.4397798 -0.05699817 0.125404228 2 NA
# Perform MIES loop until terminated. This gives an expected `terminated` error
repeat {
offspring <- mies_generate_offspring(oi, 2, op.parent, op.m, op.r)
mies_evaluate_offspring(oi, offspring)
mies_survival_plus(oi, 3, op.survival)
}
# Best result:
oi$archive$data[which.max(Obj)]
#> x y dob eol Obj x_domain timestamp batch_nr
#> 1: 2.021887 2.164051 46 NA 1.946115 <list[2]> 2021-02-15 01:56:23 46