Function that runs one GA metaepoch. Wrapper function for GA::ga.

Usage

ga_metaepoch(config_ga)

Arguments

config_ga

list of GA::ga params

Value

list with named fields: solution, population, value, fitness_values, context or NULL. A solution is a value of the decision variable giving the best fitness. A population is a matrix representing final population. A value is the value of a fitness function for the solution. A fitness_values is a vector of fitness values for the final population. A context is a list with internal state of the metaepoch (or NULL if it's not necessary). NULL can be returned if GA::ga fails and "ignore_errors" is TRUE in the config.

Examples

tree_height <- 3
empty_config_ga <- lapply(1:tree_height, function(x) {
  list("ignore_errors" = TRUE)
})
ga_metaepoch(empty_config_ga)
#> function (fitness, deme, lower, upper, minimize) 
#> {
#>     ga_fitness <- ifelse(minimize, function(x) {
#>         -1 * fitness(x)
#>     }, fitness)
#>     config <- config_ga[[deme@level]]
#>     ignore_errors <- ifelse(is.null(config$ignore_errors), TRUE, 
#>         config$ignore_errors)
#>     legal_passed_param_names <- Filter(function(name) {
#>         name %in% methods::formalArgs(GA::ga)
#>     }, names(config))
#>     params <- list(maxiter = 5, popSize = nrow(deme@population))
#>     for (param_name in legal_passed_param_names) {
#>         params[param_name] <- config[param_name]
#>     }
#>     params$fitness <- ga_fitness
#>     params$lower <- lower
#>     params$upper <- upper
#>     params$suggestions <- deme@population
#>     params$type <- "real-valued"
#>     params$monitor <- FALSE
#>     tryCatch({
#>         GA <- do.call(GA::ga, params)
#>     }, error = function(e) {
#>         if (ignore_errors) {
#>             warning("GA::ga failed with error: ", e)
#>             return(NULL)
#>         }
#>         else {
#>             stop("GA::ga failed with error: ", e)
#>         }
#>     })
#>     value <- ifelse(minimize, GA@fitnessValue * -1, GA@fitnessValue)
#>     fitness_values <- ifelse(minimize, GA@fitness * -1, GA@fitness)
#>     list(solution = c(GA@solution[1, ]), population = GA@population, 
#>         value = value, fitness_values = fitness_values, context = NULL)
#> }
#> <bytecode: 0x5840f1c5f740>
#> <environment: 0x5840f4754b40>