nsga2: NSGAII algorithm

In jiripetrlik/r-multiobjective-evolutionary-algorithms: Multiobjective Genetic Algorithms

Description

Use NSGAII algorithm to solve the multiobjective optimization problem.

Usage

nsga2(objective_functions_list, chromosome_size,
  chromosome_type = "binary",
  population_size = length(objective_functions_list) * 40,
  number_of_iterations = 100, nc = 2, mutation_probability = 0.05,
  uniform_mutation_sd = 0.01)

Arguments

objective_functions_list	List of objective functions
chromosome_size	Size of chromosome which represents candidate solutions
chromosome_type	Chromosome type ("binary" or "numeric")
population_size	Number of solutions evaluated in one iteration of genetic algorithm
number_of_iterations	Number of iterations (generations) of genetic algorithm
nc	NC for SBX crossover (valid if "numeric" chromosome is used)
mutation_probability	Probability of mutation (valid if "binary" chromosome is used)
uniform_mutation_sd	Standard deviation of mutation (valid if "numeric" chromosome is used)

Value

List which contains results of NSGAII:

values - Matrix with objective functions values for nondominated solutions. Each row represents one nondominated solution and each column one objective function.

nondominated_solutions - Chromosomes of nondominated solutions

statistics - Statistics about run of genetic algorithm

parameters - Parameters of genetic algorithm

jiripetrlik/r-multiobjective-evolutionary-algorithms documentation built on April 27, 2020, 12:12 p.m.