Description Usage Arguments Details Value Author(s) References See Also Examples
Maximization of a fitness function using islands genetic algorithms (ISLGAs). This is a distributed multiplepopulation GA, where the population is partitioned into several subpopulations and assigned to separated islands. Independent GAs are executed in each island, and only occasionally sparse exchanges of individuals are performed among the islands. In principle islands can evolve sequentially, but increased computational efficiency is obtained by running GAs in parallel on each island. The latter is called island parallel GAs (ISLPGAs) and it is used by default.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29  gaisl(type = c("binary", "realvalued", "permutation"),
fitness, ...,
lower, upper, nBits,
population = gaControl(type)$population,
selection = gaControl(type)$selection,
crossover = gaControl(type)$crossover,
mutation = gaControl(type)$mutation,
popSize = 100,
numIslands = 4,
migrationRate = 0.1,
migrationInterval = 10,
pcrossover = 0.8,
pmutation = 0.1,
elitism = base::max(1, round(popSize/numIslands*0.05)),
updatePop = FALSE,
postFitness = NULL,
maxiter = 1000,
run = maxiter,
maxFitness = Inf,
names = NULL,
suggestions = NULL,
optim = FALSE,
optimArgs = list(method = "LBFGSB",
poptim = 0.05,
pressel = 0.5,
control = list(fnscale = 1, maxit = 100)),
parallel = TRUE,
monitor = if(interactive()) gaislMonitor else FALSE,
seed = NULL)

type 
the type of genetic algorithm to be run depending on the nature of decision variables. Possible values are:

fitness 
the fitness function, any allowable R function which takes as input an individual 
... 
additional arguments to be passed to the fitness function. This allows to write fitness functions that keep some variables fixed during the search. 
lower 
a vector of length equal to the decision variables providing the lower bounds of the search space in case of realvalued or permutation encoded optimizations. Formerly this argument was named 
upper 
a vector of length equal to the decision variables providing the upper bounds of the search space in case of realvalued or permutation encoded optimizations. Formerly this argument was named 
nBits 
a value specifying the number of bits to be used in binary encoded optimizations. 
population 
an R function for randomly generating an initial population. See 
numIslands 
an integer value specifying the number of islands to be used in a ring topology, in which each island is connected unidirectionally with another island, hence forming a single continuous pathway. 
migrationRate 
a value in the range $[0,1]$ providing the proportion of individuals that should migrate between the islands. 
migrationInterval 
an integer value specifying the number of iterations at which exchange of individuals takes place. 
selection 
an R function performing selection, i.e. a function which generates a new population of individuals from the current population probabilistically according to individual fitness. See 
crossover 
an R function performing crossover, i.e. a function which forms offsprings by combining part of the genetic information from their parents. See 
mutation 
an R function performing mutation, i.e. a function which randomly alters the values of some genes in a parent chromosome. See 
popSize 
the population size. 
updatePop 
a logical defaulting to 
postFitness 
a userdefined function which, if provided, receives the current 
pcrossover 
the probability of crossover between pairs of chromosomes. Typically this is a large value and by default is set to 0.8. 
pmutation 
the probability of mutation in a parent chromosome. Usually mutation occurs with a small probability, and by default is set to 0.1. 
elitism 
the number of best fitness individuals to survive at each generation. By default the top 5% individuals in each island will survive at each iteration. 
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. 
maxFitness 
the upper bound on the fitness function after that the GA search is interrupted. 
names 
a vector of character strings providing the names of decision variables. 
suggestions 
a matrix of solutions strings to be included in the initial population. If provided the number of columns must match the number of decision variables. 
optim 
a logical defaulting to 
optimArgs 
a list controlling the local search algorithm with the following components:

parallel 
An optional argument which allows to specify if the Islands Genetic Algorithm should be run sequentially or in parallel. For a single machine with multiple cores, possible values are:
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, evolution of GAs on each island 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 
monitor 
a logical or an R function which takes as input the current state of the 
seed 
an integer value containing the random number generator state. This argument can be used to replicate the results of a ISLGA search. Note that if parallel computing is required, the doRNG package must be installed. 
Genetic algorithms (GAs) are stochastic search algorithms inspired by the basic principles of biological evolution and natural selection. GAs simulate the evolution of living organisms, where the fittest individuals dominate over the weaker ones, by mimicking the biological mechanisms of evolution, such as selection, crossover and mutation.
The gaisl
function implements the islands GAs approach, where the population is partitioned into several subpopulations and assigned to separated islands. Independent GAs are executed in each island, and only occasionally sparse exchanges of individuals are performed among the islands. The algorithm can be run in parallel or sequentially.
For more information on GAs see ga
.
Returns an object of class gaislclass
. See gaislclass
for a description of available slots information.
Luca Scrucca luca.scrucca@unipg.it
Luque G., Alba E. (2011) Parallel Genetic Algorithms: Theory and Real World Applications. Springer.
Luke S. (2013) Essentials of Metaheuristics, 2nd edition. Lulu. Freely available at http://cs.gmu.edu/~sean/book/metaheuristics/.
Scrucca, L. (2017) On some extensions to GA package: hybrid optimisation, parallelisation and islands evolution. The R Journal, 9/1, 187206. https://journal.rproject.org/archive/2017/RJ2017008
summary,gaislmethod
,
plot,gaislmethod
,
gaislclass
,
ga
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21  ## Not run:
# twodimensional Rastrigin function
Rastrigin < function(x1, x2)
{
20 + x1^2 + x2^2  10*(cos(2*pi*x1) + cos(2*pi*x2))
}
x1 < x2 < seq(5.12, 5.12, by = 0.1)
f < outer(x1, x2, Rastrigin)
persp3D(x1, x2, f, theta = 50, phi = 20)
filled.contour(x1, x2, f, color.palette = jet.colors)
GA < gaisl(type = "realvalued",
fitness = function(x) Rastrigin(x[1], x[2]),
lower = c(5.12, 5.12), upper = c(5.12, 5.12),
popSize = 80, maxiter = 500,
numIslands = 4, migrationInterval = 50)
summary(GA)
plot(GA)
## End(Not run)

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