Initialize populations in genetic algorithms

Build an initial population set for genetic algorithms

Usage

build_gabin_population(x, ...)

log_gabin_population(x, ...)

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 a ts 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 a ts object and sets the initial probability to the natural logarithm of the length of the time series.

See also

GA::gabin_Population(), segment_ga()

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).