single_objective_ga: Single objective genetic algorithm

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

Description

Use single objective genetic algorithm to find an optimum for the specified objective function. Candidate solutions are represented as logical or numeric vectors.

Usage

single_objective_ga(objective_function, chromosome_size,
  chromosome_type = "binary", population_size = 100,
  number_of_iterations = 100, elitism = TRUE, nc = 2,
  mutation_probability = 0.05, uniform_mutation_sd = 0.01)

Arguments

objective_function	Objective function
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
elitism	Use elitism
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 single objective genetic algorithm:

value - Value of objective function for the best solution

best_solution - Chromosome which represents the best solution

best_solution_index - Index of the best solution in population

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.