Main genetic algorithm loop for genomic data optimisation

Runs a generational multi-objective genetic algorithm combining initialization, fitness evaluation, NSGA-II selection, crossover, mutation and NSGA-II replacement.

Usage

bioga_main_cpp(
  genomic_data,
  population_size,
  num_generations,
  crossover_rate,
  eta_c,
  mutation_rate,
  num_parents,
  num_offspring,
  num_to_replace,
  weights,
  seed = NULL,
  clusters = NULL,
  network = NULL,
  labels = NULL,
  representation = "real"
)

Arguments

genomic_data

Numeric matrix of genomic data (rows: genes, columns: samples).

population_size

Number of individuals in the population.

num_generations

Number of generations to run.

crossover_rate

Probability of crossover.

eta_c

SBX distribution index (real representation).

mutation_rate

Base probability of mutation.

num_parents

Number of parents selected per generation.

num_offspring

Number of offspring generated per generation.

num_to_replace

Maximum number of offspring admitted per generation.

weights

Numeric vector of objective weights.

seed

Optional random seed for reproducibility.

clusters

Optional vector of gene cluster assignments.

network

Optional symmetric gene network adjacency matrix.

labels

Optional class labels (required when representation = "binary"). Accepted as an integer, numeric, factor, character or logical vector.

representation

Either "real" or "binary".

Value

A list with population (final population), fitness (final fitness matrix), history (best first objective per generation), best (best individual), best_fitness, pareto_population and pareto_fitness (the first non-dominated front of the final population).

References

Deb K, Pratap A, Agarwal S, Meyarivan T (2002). "A fast and elitist multiobjective genetic algorithm: NSGA-II." IEEE Transactions on Evolutionary Computation, 6(2), 182-197. doi:10.1109/4235.996017

See also

initialize_population_cpp(), evaluate_fitness_cpp(), selection_cpp(), crossover_cpp(), mutation_cpp(), replacement_cpp()

Examples

genomic_data <- matrix(rnorm(100), nrow = 10, ncol = 10)
result <- BioGA::bioga_main_cpp(genomic_data, population_size = 50,
  num_generations = 10, crossover_rate = 0.9, eta_c = 20.0,
  mutation_rate = 0.1, num_parents = 20, num_offspring = 20,
  num_to_replace = 10, weights = c(1.0, 0.5), seed = 123)