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https://github.com/mlr-org/miesmuschel
Flexible Mixed Integer Evolutionary Strategies
https://github.com/mlr-org/miesmuschel
Last synced: about 2 months ago
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Flexible Mixed Integer Evolutionary Strategies
- Host: GitHub
- URL: https://github.com/mlr-org/miesmuschel
- Owner: mlr-org
- License: other
- Created: 2020-12-15T15:38:22.000Z (about 4 years ago)
- Default Branch: master
- Last Pushed: 2024-08-21T13:06:09.000Z (4 months ago)
- Last Synced: 2024-10-25T22:53:59.021Z (about 2 months ago)
- Language: R
- Size: 1.74 MB
- Stars: 15
- Watchers: 4
- Forks: 4
- Open Issues: 51
-
Metadata Files:
- Readme: README.md
- Changelog: NEWS.md
- License: LICENSE
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README
# miesmuschel: Mixed Integer Evolution Strategies
[](https://github.com/mlr-org/miesmuschel/actions/workflows/check.yml)
[](https://app.codecov.io/github/mlr-org/miesmuschel)
[](https://CRAN.R-project.org/package=miesmuschel)
[](https://CRAN.R-project.org/package=miesmuschel)
## What does it do?
`miesmuschel` provides evolutionary black box optimization algorithms, building on the [`bbotk`](https://bbotk.mlr-org.com/) 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.
## What does it do? (poem version)
(also giving a hint on how to pronounce "`miesmuschel`")
The R software package `miesmuschel`
Offers opt-algorithms, so crucial
[`bbotk`](https://bbotk.mlr-org.com/) 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!
## Project Status
Although `miesmuschel` is currently still evolving, it can already be used for optimization. All exported functions are [thoroughly documented](https://mlr-org.github.io/miesmuschel/reference/index.html).
## Installation
Install the github version, using `remotes`:
```r
remotes::install_github("mlr-org/miesmuschel")
```
## Some Code to Get Started
### Preparation
```r
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$y)^2)
list(Obj = z)
},
domain = ps(x = p_dbl(-2, 4), y = p_dbl(-2, 4)),
codomain = ps(Obj = p_dbl(tags = "maximize"))
)
```
### Using `bbotk::Optimizer` Object
This is the recommended way of using `miesmuschel`.
```r
# Get a new OptimInstance
oi <- OptimInstanceSingleCrit$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 1.990703
```
### Using `mies_*` Functions Directly
This gives more flexibility when designing ES algorithms, but it is also more verbose and error-prone.
```r
# 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 2021-02-15 01:56:23 46
```