public Individual Recombine(Individual b)
{
Individual res = null;
do
try
{
bool[] a_genes = new bool[fe.n];
foreach (int i in genes)
a_genes[i] = true;
bool[] b_genes = new bool[b.fe.n];
foreach (int i in b.genes)
b_genes[i] = true;
int s = prms.rand.Next(fe.n), t = prms.rand.Next(fe.n);
int gen;
for (int i = 0; i < t; ++i)
{
gen = (s + i) % fe.n;
a_genes[gen] ^= b_genes[gen];
b_genes[gen] ^= a_genes[gen];
a_genes[gen] ^= b_genes[gen];
}
List<int> new_genes = new List<int>();
for (int i = 0; i < fe.n; ++i)
if (a_genes[i])
new_genes.Add(i);
res = new Individual(new_genes, fe, prms);
}
catch (CalculationException e) { }
while (res == null);
return res;
}
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 Individual Mutate()
{
Individual res = null;
do
try
{
bool[] gene_array = new bool[fe.n];
foreach (int i in genes)
gene_array[i] = true;
List<int> new_genes = new List<int>();
for (int i = 0; i < fe.n; ++i)
{
if (gene_array[i])
{
if (prms.rand.NextDouble() >= prms.mut_rate_deact)
new_genes.Add(i);
}
else
{
if (!too_many_genes && prms.rand.NextDouble() < prms.mut_rate_act)
new_genes.Add(i);
}
}
res = new Individual(new_genes, 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());
}
