Initialize populations in genetic algorithms
build_gabin_population.Rd
Build an initial population set for genetic algorithms
Arguments
- x
a numeric vector coercible into a stats::ts object
- ...
arguments passed to methods
Value
A function
that can be passed to the population
argument of
GA::ga()
(through segment_ga()
)
Details
Genetic algorithms require a method for randomly generating initial
populations (i.e., a first generation).
The default method used by GA::ga()
for changepoint detection is usually
GA::gabin_Population()
, which selects candidate changepoints uniformly at
random with probability 0.5.
This leads to an initial population with excessively large candidate
changepoint sets (on the order of \(n/2\)), which makes the genetic
algorithm slow.
build_gabin_population()
takes ats
object and runs several fast changepoint detection algorithms on it, then sets the initial probability to 3 times the average value of the size of the changepoint sets returned by those algorithms. This is a conservative guess as to the likely size of the optimal changepoint set.log_gabin_population()
takes ats
object and sets the initial probability to the natural logarithm of the length of the time series.
Examples
# Build a function to generate the population
f <- build_gabin_population(CET)
# Segment the time series using the population generation function
segment(CET, method = "ga", population = f, maxiter = 5)
#> Seeding initial population with probability: 0.0273224043715847
#> ℹ A tidycpt object. Segmenter ↓
#> An object of class "ga"
#>
#> Call:
#> GA::ga(type = "binary", fitness = obj_fun, nBits = n, population = ..1, maxiter = 5)
#>
#> Available slots:
#> [1] "data" "model_fn_args" "call" "type"
#> [5] "lower" "upper" "nBits" "names"
#> [9] "popSize" "iter" "run" "maxiter"
#> [13] "suggestions" "population" "elitism" "pcrossover"
#> [17] "pmutation" "optim" "fitness" "summary"
#> [21] "bestSol" "fitnessValue" "solution"
#> ℹ Model: A meanshift_norm model with 5 region(s).
#> → Each region has 1 parameter(s).
#> → The model has 1 global parameter(s).
f <- log_gabin_population(CET)
segment(CET, method = "ga", population = f, maxiter = 10)
#> Seeding initial population with probability: 0.0161274134792387
#> ℹ A tidycpt object. Segmenter ↓
#> An object of class "ga"
#>
#> Call:
#> GA::ga(type = "binary", fitness = obj_fun, nBits = n, population = ..1, maxiter = 10)
#>
#> Available slots:
#> [1] "data" "model_fn_args" "call" "type"
#> [5] "lower" "upper" "nBits" "names"
#> [9] "popSize" "iter" "run" "maxiter"
#> [13] "suggestions" "population" "elitism" "pcrossover"
#> [17] "pmutation" "optim" "fitness" "summary"
#> [21] "bestSol" "fitnessValue" "solution"
#> ℹ Model: A meanshift_norm model with 5 region(s).
#> → Each region has 1 parameter(s).
#> → The model has 1 global parameter(s).