rmoo (version 0.1.6)

nsga3-class: Virtual Class 'nsga3 - Simple Class for subassigment Values'

Description

The class 'nsga3' is a simple class union (setClassUnion()) of 'numeric', 'logical' and 'matrix'.

Slots

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.

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.

Objects from the Class

Since it is a virtual Class, no objects may be created from it.

Examples

# NOT RUN {
showClass('nsga3')
# }