gaisl-class.RdAn S4 class for islands genetic algorithms (ISLGAs)
Objects can be created by calls to the gaisl function.
callan object of class "call" representing the matched call;
typea character string specifying the type of genetic algorithm used;
lowera vector providing for each decision variable the lower bounds of the search space in case of real-valued or permutation encoded optimisations. Formerly this slot was named min;
uppera vector providing for each decision variable the upper bounds of the search space in case of real-valued or permutation encoded optimizations. Formerly this slot was named max;
nBitsa value specifying the number of bits to be used in binary encoded optimizations;
namesa vector of character strings providing the names of decision variables (optional);
popSizethe population size;
numIslandsthe number of islands;
migrationRatethe migration rate;
migrationIntervalthe migration interval;
maxiterthe maximum number of ISLGA iterations before the search is halted;
runthe number of consecutive generations without any improvement in the best fitness value before the ISLGA is stopped;
maxiterthe maximum number of iterations to run before the GA search is halted;
suggestionsa matrix of user provided solutions and included in the initial population;
elitismthe number of best fitness individuals to survive at each generation;
pcrossoverthe crossover probability;
pmutationthe mutation probability;
optima logical specifying whether or not a local search using general-purpose optimisation algorithms should be used;
islandsa list containing the objects of class ga corresponding to each island GA evolution;
summarya list of matrices of summary statistics for fitness values at each iteration (along the rows). Each element of the list corresponds to the evolution of an island;
fitnessValuesa list of best fitness values found in each island at the final iteration;
solutionsa list of matrices, one for each island, containing the values of the decision variables giving the best fitness at the final iteration;
fitnessValuethe best fitness value at the final iteration;
solutiona matrix containing the values of the decision variables giving the best fitness at the final iteration.
For examples of usage see gaisl.