gaisl-class.Rd
An S4 class for islands genetic algorithms (ISLGAs)
Objects can be created by calls to the gaisl
function.
call
an object of class "call"
representing the matched call;
type
a character string specifying the type of genetic algorithm used;
lower
a 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
;
upper
a 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
;
nBits
a value specifying the number of bits to be used in binary encoded optimizations;
names
a vector of character strings providing the names of decision variables (optional);
popSize
the population size;
numIslands
the number of islands;
migrationRate
the migration rate;
migrationInterval
the migration interval;
maxiter
the maximum number of ISLGA iterations before the search is halted;
run
the number of consecutive generations without any improvement in the best fitness value before the ISLGA is stopped;
maxiter
the maximum number of iterations to run before the GA search is halted;
suggestions
a matrix of user provided solutions and included in the initial population;
elitism
the number of best fitness individuals to survive at each generation;
pcrossover
the crossover probability;
pmutation
the mutation probability;
optim
a logical specifying whether or not a local search using general-purpose optimisation algorithms should be used;
islands
a list containing the objects of class ga
corresponding to each island GA evolution;
summary
a 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;
fitnessValues
a list of best fitness values found in each island at the final iteration;
solutions
a list of matrices, one for each island, containing the values of the decision variables giving the best fitness at the final iteration;
fitnessValue
the best fitness value at the final iteration;
solution
a matrix containing the values of the decision variables giving the best fitness at the final iteration.
For examples of usage see gaisl
.