# Genetic Algorithm Wrapper Function for recmap higher-level function for `recmap` using a Genetic Algorithm as metaheuristic. ```r recmapGA( V, fitness = .recmap.fitness, pmutation = 0.25, popSize = 10 * nrow(Map), maxiter = 10, run = maxiter, monitor = if (interactive()) { gaMonitor } else FALSE, parallel = FALSE, ... ) ``` ## Arguments - `V`: defines the input map regions formatted as `data.frame` having the column names `c('x', 'y', 'dx', 'dy', 'z', 'name')` as described above. V could also be considered as the nodes of the pseudo dual. - `fitness`: the fitness function, any allowable R function which takes as input an individual `string` representing a potential solution, and returns a numerical value describing its ``fitness''. - `pmutation`: the probability of mutation in a parent chromosome. Usually mutation occurs with a small probability, and by default is set to 0.1. - `popSize`: the population size. - `maxiter`: the maximum number of iterations to run before the GA search is halted. - `run`: the number of consecutive generations without any improvement in the best fitness value before the GA is stopped. - `monitor`: a logical or an R function which takes as input the current state of the `ga-class` object and show the evolution of the search. By default, for interactive sessions the function `gaMonitor` prints the average and best fitness values at each iteration. If set to `plot` these information are plotted on a graphical device. Other functions can be written by the user and supplied as argument. In non interactive sessions, by default `monitor = FALSE` so any output is suppressed. - `parallel`: An optional argument which allows to specify if the Genetic Algorithm should be run sequentially or in parallel. For a single machine with multiple cores, possible values are: * a logical value specifying if parallel computing should be used (`TRUE`) or not (`FALSE`, default) for evaluating the fitness function; * a numerical value which gives the number of cores to employ. By default, this is obtained from the function `detectCores`; * a character string specifying the type of parallelisation to use. This depends on system OS: on Windows OS only `"snow"` type functionality is available, while on Unix/Linux/Mac OSX both `"snow"` and `"multicore"` (default) functionalities are available. In all the cases described above, at the end of the search the cluster is automatically stopped by shutting down the workers. If a cluster of multiple machines is available, evaluation of the fitness function can be executed in parallel using all, or a subset of, the cores available to the machines belonging to the cluster. However, this option requires more work from the user, who needs to set up and register a parallel back end. In this case the cluster must be explicitly stopped with `stopCluster`. - `...`: additional arguments to be passed to the fitness function. This allows to write fitness functions that keep some variables fixed during the search. ## Returns returns a list of the input `Map`, the solution of the `ga` function, and a `recmap` object containing the cartogram. ## Examples ```r ## The default fitnes function is currently defined as function(idxOrder, Map, ...){ Cartogram <- recmap(Map[idxOrder, ]) # a map region could not be placed; # accept only feasible solutions! if (sum(Cartogram$topology.error == 100) > 0){return (0)} 1 / sum(Cartogram$relpos.error) } ## use Genetic Algorithms (GA >=3.0.0) as metaheuristic set.seed(1) ## https://github.com/luca-scr/GA/issues/52 if (Sys.info()['machine'] == "arm64") GA::gaControl(useRcpp = FALSE) res <- recmapGA(V = checkerboard(4), pmutation = 0.25) op <- par(mfrow = c(1, 3)) plot(res$Map, main = "Input Map") plot(res$GA, main="Genetic Algorithm") plot(res$Cartogram, main = "Output Cartogram") ## US example getUS_map <- function(){ usa <- data.frame(x = state.center$x, y = state.center$y, # make the rectangles overlapping by correcting # lines of longitude distance. dx = sqrt(state.area) / 2 / (0.8 * 60 * cos(state.center$y * pi / 180)), dy = sqrt(state.area) / 2 / (0.8 * 60), z = sqrt(state.area), name = state.name) usa$z <- state.x77[, 'Population'] US.Map <- usa[match(usa$name, c('Hawaii', 'Alaska'), nomatch = 0) == 0, ] class(US.Map) <- c('recmap', 'data.frame') US.Map } ## Not run: # takes 34.268 seconds on CRAN res <- recmapGA(V = getUS_map(), maxiter = 5) op <- par(ask = TRUE) plot(res) par(op) summary(res) ## End(Not run) ``` ## References Luca Scrucca (2013). GA: A Package for Genetic Algorithms in R. Journal of Statistical Software, 53(4), 1-37. tools:::Rd_expr_doi("10.18637/jss.v053.i04") .

recmapGA function