Main genetic algorithm loop for genomic data optimization
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
)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 (default: 20.0).
- mutation_rate
Base probability of mutation.
- num_parents
Number of parents to select per generation.
- num_offspring
Number of offspring to generate per generation.
- num_to_replace
Number of individuals to replace per generation.
- weights
Numeric vector of weights for multi-objective fitness.
- seed
Optional random seed for reproducibility.
- clusters
Optional vector of gene cluster assignments.
- network
Optional matrix of gene network constraints.
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)
#> Current front size: 1
#> Current front size: 1
#> Current front size: 1
#> Current front size: 1
#> Current front size: 1
#> Current front size: 1
#> Current front size: 1
#> Current front size: 0
#> Warning: No non-dominated individuals found. Using full population for selection.
#> Current front size: 1
#> Current front size: 1