The NSGA-II merges the current population and the generated offspring and reduces it by means of the following procedure: It first applies the non dominated sorting algorithm to obtain the nondominated fronts. Starting with the first front, it fills the new population until the i-th front does not fit. It then applies the secondary crowding distance criterion to select the missing individuals from the i-th front.
nsga2(
fitness.fun,
n.objectives = NULL,
n.dim = NULL,
minimize = NULL,
lower = NULL,
upper = NULL,
mu = 100L,
lambda = mu,
mutator = setup(mutPolynomial, eta = 25, p = 0.2, lower = lower, upper = upper),
recombinator = setup(recSBX, eta = 15, p = 0.7, lower = lower, upper = upper),
terminators = list(stopOnIters(100L)),
...
)[ecr_multi_objective_result]
[function]
The fitness function.
[integer(1)]
Number of objectives of obj.fun.
Optional if obj.fun is a benchmark function from package smoof.
[integer(1)]
Dimension of the decision space.
[logical(n.objectives)]
Logical vector with ith entry TRUE if the ith objective of fitness.fun
shall be minimized. If a single logical is passed, it is assumed to be valid
for each objective.
[numeric]
Vector of minimal values for each parameter of the decision space in case
of float or permutation encoding.
Optional if obj.fun is a benchmark function from package smoof.
[numeric]
Vector of maximal values for each parameter of the decision space in case
of float or permutation encoding.
Optional if obj.fun is a benchmark function from package smoof.
[integer(1)]
Number of individuals in the population.
Default is 100.
[integer(1)]
Offspring size, i.e., number of individuals generated by variation operators
in each iteration.
Default is 100.
[ecr_mutator]
Mutation operator of type ecr_mutator.
[ecr_recombinator]
Recombination operator of type ecr_recombinator.
[list]
List of stopping conditions of type “ecr_terminator”.
Default is to stop after 100 iterations.
[any]
Further arguments passed down to fitness function.
Deb, K., Pratap, A., and Agarwal, S. A Fast and Elitist Multiobjective Genetic Algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation, 6 (8) (2002), 182-197.