nsga2 {ecr} R Documentation

## Implementation of the NSGA-II EMOA algorithm by Deb.

### Description

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.

### Usage

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)), ...)


### Arguments

 fitness.fun [function] The fitness function. n.objectives [integer(1)] Number of objectives of obj.fun. Optional if obj.fun is a benchmark function from package smoof. n.dim [integer(1)] Dimension of the decision space. minimize [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. lower [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. upper [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. mu [integer(1)] Number of individuals in the population. Default is 100. lambda [integer(1)] Offspring size, i.e., number of individuals generated by variation operators in each iteration. Default is 100. mutator [ecr_mutator] Mutation operator of type ecr_mutator. recombinator [ecr_recombinator] Recombination operator of type ecr_recombinator. terminators [list] List of stopping conditions of type “ecr_terminator”. Default is to stop after 100 iterations. ... [any] Further arguments passed down to fitness function.

### Value

[ecr_multi_objective_result]

### Note

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.

### References

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.

[Package ecr version 2.1.0 Index]