def getFrontParetoWithoutGraphic(start_fct, operator_fct, generation_fct, nb_functions,
nb_iterations, neighboring_size, problem_size, max_decisions_maj, delta_neighbourhood, CR, search_space, F, distrib_index_n, pm, manage_archive, file_to_write, param_print_every, sleeptime=10):
global param, archiveOK
if(manage_archive):
archiveOK = True
#random initialisation
init_decisions = init_to.initRandom(generation_fct, nb_functions, problem_size, search_space)
#algorithm parameters
param = [start_fct, nb_functions, nb_iterations, neighboring_size, init_decisions, problem_size, max_decisions_maj, delta_neighbourhood, CR, search_space, F, distrib_index_n, pm, operator_fct, file_to_write, param_print_every]
#launch the algorithm
result = runTcheby()
#return the approximation of the pareto front and the archive if managed
return result
def getFrontParetoWithGraphic(problem_title, start_fct, operator_fct, generation_fct, pareto_front_fct, nb_functions,
nb_iterations, neighboring_size, problem_size, max_decisions_maj, delta_neighbourhood, CR, search_space, F, distrib_index_n, pm, manage_archive, file_to_write, sleeptime=10):
global param, archiveOK
if(manage_archive):
archiveOK = True
#random initialisation
init_decisions = init_to.initRandom(generation_fct, nb_functions, problem_size, search_space)
#algorithm parameters
param = [start_fct, nb_functions, nb_iterations, neighboring_size, init_decisions, problem_size, max_decisions_maj, delta_neighbourhood, CR, search_space, F, distrib_index_n, pm, operator_fct, "none", -1]
#function that will be called by runAnimatedGraph before it's end
end_function = getResult
#launch the graphic view and the algorithm
result = gph.runAnimatedGraph(runTcheby,end_function, pareto_front_fct, problem_title ,"f1" ,"f2", sleep=sleeptime)
#return the approximation of the pareto front and the archive if managed
return result
def getFrontParetoWithoutGraphic(start_fct, operator_fct, generation_fct, nb_functions,
nb_iterations, neighboring_size, problem_size, max_decisions_maj, delta_neighbourhood, CR, search_space, F, distrib_index_n, pm, manage_archive, nb_samples, training_neighborhood_size, strategy, file_to_write, filter_strat, free_eval, param_print_every, file_to_writeR2, filenameDIR, filenameSCORE, sleeptime=10):
global param, archiveOK
if(manage_archive):
archiveOK = True
#random initialisation
init_decisions = init_to.initRandom(generation_fct, nb_functions, problem_size, search_space)
#algorithm parameters
param = [start_fct, nb_functions, nb_iterations, neighboring_size, init_decisions, problem_size, max_decisions_maj, delta_neighbourhood, CR, search_space, F, distrib_index_n, pm, operator_fct, nb_samples, training_neighborhood_size, strategy, file_to_write, filter_strat, free_eval, param_print_every, file_to_writeR2, filenameDIR, filenameSCORE]
#function that will be called by runAnimatedGraph before it's end
end_function = getResult
#launch the graphic view and the algorithm
result = runTcheby()
#return the approximation of the pareto front and the archive if managed
return result
