public Population(FitnessEstimator fe, int population_size,
double recombination_rate, double mut_rate_act, double mut_rate_deact, double start_density)
{
this.population_size = population_size;
this.recombination_rate = recombination_rate;
EvolutionParams prms = new EvolutionParams(mut_rate_act, mut_rate_deact);
population = new SortedList<double, Individual>();
Individual temp;
for (int i = 0; i < population_size; ++i)
{
temp = Individual.RandomIndividual(fe, start_density, prms);
population.Add(temp.Fitness, temp);
}
}
public OptimizationTree(FitnessEstimator fe, List<int> R0, List<int> Rb, List<int> approximation = null, FluxPattern max_approx = null, bool output = true)
{
this.dot_tree = new LinkedList<string>();
this.opt_genes = new LinkedList<List<int>>();
this.opt_max_size = fe.DoIncrease ? int.MaxValue : int.MinValue;
this.opt_r_size = fe.DoIncrease ? int.MinValue : int.MaxValue;
this.fe = fe;
this.T = new List<int>(R0);
this.D = new List<int>(Rb);
bool[] allowed = new bool[fe.n];
for (int i = 0; i < fe.n; ++i)
allowed[i] = true;
foreach (int r in R0)
allowed[r] = false;
this.max_allowed = new FluxPattern(allowed, false);
if (approximation != null)
Update(approximation, max_approx);
this.output = output;
this.n_updates = 0;
List<int> reactions = new List<int>();
for (int i = 0; i < Rb.Count; ++i)
reactions.Add(i);
this.order = reactions.OrderBy(x => System.Guid.NewGuid()).ToList();
Optimize(fe.DoIncrease ? this.D : new List<int>(), 0);
}
public Individual(List<int> genes, FitnessEstimator fe, EvolutionParams prms)
{
this.fe = fe;
this.prms = prms;
this.fitness = fe.CalculateFitness(genes, out this.max, out this.genes, out this.too_many_genes);
}
public static Individual RandomIndividual(FitnessEstimator fe, double density, EvolutionParams prms)
{
Individual res = null;
do
try
{
List<int> r = new List<int>();
for (int i = 0; i < fe.n; ++i)
if (prms.rand.NextDouble() < density)
r.Add(i);
res = new Individual(r, fe, prms);
}
catch (CalculationException e) { }
while (res == null);
return res;
}
public static void Optimize(double[,] S, bool[] rev, bool increase, LinkedList<FluxPattern> witnesses, FluxPattern max, FluxPattern frev, IIntCoupling coupling, List<int> R0, List<int> Rb, bool lp, double max_value, double tolerance, bool output, int population_size, int generations, double recomb_rate, double mut_rate_act, double mut_rate_deact, double start_density, out LinkedList<String> dot_tree)
{
IIntFCACalculator<FluxPattern> fcacalculator = lp ? (IIntFCACalculator<FluxPattern>)new LPIntFCACalculator(S, rev, max_value, tolerance) : (IIntFCACalculator<FluxPattern>)new MILPIntFCACalculator(S, rev, max_value, tolerance);
FitnessEstimator fe = new FitnessEstimator(R0, Rb, increase, rev.Length, fcacalculator, witnesses, max, frev, coupling);
List<int> approximation = null;
FluxPattern max_approx = null;
if (population_size > 0)
{
Population population = new Population(fe, population_size, recomb_rate, mut_rate_act, mut_rate_deact, start_density);
for (int i = 1; i < generations; ++i)
population.evolve();
Individual best = population.FittestIndividual;
if (best.Fitness < 0)
{
approximation = best.Genes;
max_approx = best.Max;
}
}
OptimizationTree tree = new OptimizationTree(fe, R0, Rb, approximation, max_approx, output);
dot_tree = tree.DotTree;
LinkedList<List<int>> opt_solutions = tree.OptGenes;
Console.WriteLine("\n\n\nThere are {0} optimal solutions.\n\t{1} reactions are unblocked.\n\tYou have to use {2} drugs.", opt_solutions.Count, tree.OptMaxSize, tree.OptRSize);
if (opt_solutions.Count > 0)
{
Individual opt = new Individual(opt_solutions.First.Value, fe, new EvolutionParams());
Console.WriteLine("\n\tOne optimal solution is: {0}", opt);
}
Console.WriteLine("\n\tI had to calculate {0} out of {1} maxima to find the optima.", tree.NumberNodes, ((long)1) << Rb.Count());
}
