| gaisl-class {GA} | R Documentation |
Class "gaisl"
Description
An S4 class for islands genetic algorithms (ISLGAs)
Objects from the Class
Objects can be created by calls to the gaisl function.
Slots
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
gacorresponding 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.
Author(s)
Luca Scrucca
See Also
For examples of usage see gaisl.