{

public:

LogNormalEA(int lambda, double init_mutation_rate = 0.2)

{

this->set_mu(1);

this->set_lambda(lambda);

this->set_crossover_mutation_r("OR");

this->set_crossover_probability(0);

this->set_mutation_operator("BINOMIALSAMPLE");

this->set_selection_operator("BESTPLUS");

this->init_mutation_rate_ = init_mutation_rate;

}

void run(string folder_path, string folder_name, shared_ptr<ioh::suite::Suite<ioh::problem::IntegerSingleObjective>> suite, int eval_budget, int gene_budget, int independent_runs, unsigned rand_seed)

{

string algorithm_name = "(1+" + to_string(this->get_lambda()) + ")-lognormal-EA>0";

ioh::logger::Analyzer logger(

{ioh::trigger::on_improvement}, // trigger when the objective value improves

{}, // no additional properties

folder_path, // path to store data

folder_name, // name of the folder in path, which will be newly created

algorithm_name, // name of the algoritm

algorithm_name, // additional info about the algorithm

false // where to store x positions in the data files

);

suite->attach_logger(logger);

this->set_evaluation_budget(eval_budget);

this->set_generation_budget(gene_budget);

this->set_independent_runs(independent_runs);

this->set_seed(rand_seed);

this->run_N(suite);

}

void opt()

{

vector<int> best_ind, x;

double tmp_mr, tmp_best_mr;

double f_x, best_f_x;

this->preparation();

this->initialization();

double mutation_rate = this->init_mutation_rate_;

while (!this->termination())

{

this->update_generation();

best_f_x = std::numeric_limits<int>::min();

for (size_t i = 0; i < this->get_lambda(); ++i)

{

x = this->get_parents_population()[0];

tmp_mr = 1.0 / (1.0 + (((1.0 - mutation_rate) / mutation_rate) * exp(0.22 * operators::normal_random())));

this->set_mutation_rate(tmp_mr);

this->do_mutation(x);

f_x = this->evaluate(x);

if (f_x > best_f_x)

{

best_f_x = f_x;

best_ind = x;

tmp_best_mr = tmp_mr;

}

if (this->termination())

break;

}

if (this->termination())

break;

if (best_f_x >= this->get_parents_fitness()[0])

{

this->set_parents_population(best_ind, 0);

this->set_parents_fitness(best_f_x, 0);

}

mutation_rate = tmp_best_mr;

}

}

double init_mutation_rate_;