nsga2 {ecr} | R Documentation |
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)), ...)
fitness.fun |
[ |
n.objectives |
[ |
n.dim |
[ |
minimize |
[ |
lower |
[ |
upper |
[ |
mu |
[ |
lambda |
[ |
mutator |
[ |
recombinator |
[ |
terminators |
[ |
... |
[any] |
[ecr_multi_objective_result
]
This is a pure R implementation of the NSGA-II algorithm. It hides the regular ecr interface and offers a more R like interface while still being quite adaptable.
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.