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The class 'nsga3' is a simple class union (setClassUnion()
)
of 'numeric', 'logical' and 'matrix'.
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.
upper
a vector providing for each decision variable the upper bounds of the search space in case of real-valued or permutation encoded optimizations.
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.
front
Range in which the individual is in the front generated by the
function (non_dominated_fronts()
)
f
Fronts generated by the function (non_dominated_fronts()
)
iter
the actual (or final) iteration of NSGA search.
run
the number of consecutive generations without any improvement in the best fitness value before the NSGA is stopped.
maxiter
the maximum number of iterations to run before the NSGA search is halted.
suggestions
a matrix of user provided solutions and included in the initial population.
population
the current (or final) population.
ideal_point
Nadir point estimate used as lower bound in normalization.
worst_point
Worst point generated over generations.
smin
Index used to obtain the extreme points.
extreme_points
are selected using the ASF in the (PerformScalarizing()
).
Necessary in the nadir point generation.
worst_of_population
The worst individuals generated by objectives in the current generation.
worst_of_front
The worst individuals in the first front generated by objectives in the current generation.
nadir_point
Nadir point estimate used as upper bound in normalization.
pcrossover
the crossover probability.
pmutation
the mutation probability.
reference_points
NSGA-III uses a predefined set of reference points to ensure diversity in obtained solutions. The chosen refenrece points can be predefined in structured manner or supplied by the user. We use the Das and Dennis procedure.
fitness
the values of fitness function for the current (or final) population
summary
a matrix of summary statistics for fitness values at each iteration (along the rows).
fitnessValue
the best fitness value at the final iteration.
solution
the value(s) of the decision variables giving the best fitness at the final iteration.
Since it is a virtual Class, no objects may be created from it.