R/active_snw_search_ga.R
dot-genetic_algorithm.RdThe initial population is seeded randomly (each gene switched on with
probability params$gene_init_prob); when
params$start_with_all_positives is set, one individual containing all
positive-z-score nodes is added. The population then evolves by rank-based
selection, uniform crossover and optional mutation. Every ten generations
the worst 10% of the population is replaced with fresh random individuals,
and the previous best individual is preserved. The search stops after
params$ga_iterations generations or
once the best individual is unchanged for 50 generations.
.genetic_algorithm(network, sc, params, verbose = FALSE)A network from build_network().
A score context from build_score_context().
A list of run parameters.
Logical; emit progress messages.
A list of subnetwork objects from the best individual found.