public static LinearLinkage Apply(IRandom random, ItemArray <LinearLinkage> parents)
{
var len = parents[0].Length;
var p = random.Next(parents.Length);
var child = LinearLinkage.SingleElementGroups(len);
var remaining = new SortedSet <int>(Enumerable.Range(0, len));
do
{
var i = remaining.Min;
foreach (var g in parents[p].GetGroupForward(i))
{
if (!remaining.Contains(g))
{
continue;
}
child[i] = g;
i = g;
remaining.Remove(g);
}
child[i] = i;
remaining.Remove(i);
p = (p + 1) % parents.Length;
} while (remaining.Count > 0);
return(child);
}
public static LinearLinkage Apply(IRandom random, LinearLinkage p1, LinearLinkage p2)
{
var length = p1.Length;
var child = LinearLinkage.SingleElementGroups(length);
var bp = random.Next(length - 1);
for (var i = 0; i <= bp; i++)
{
child[i] = p1[i];
}
for (var i = bp + 1; i < length; i++)
{
child[i] = p2[i];
}
child.LinearizeTreeStructures();
return(child);
}
public static LinearLinkage Apply(IRandom random, ItemArray <LinearLinkage> parents)
{
var len = parents[0].Length;
var child = LinearLinkage.SingleElementGroups(len);
var remaining = new SortedSet <int>(Enumerable.Range(0, len));
do
{
var groups = parents.Select(x => x.GetGroupForward(remaining.Min).Where(y => remaining.Contains(y)).ToList()).ToList();
var max = groups.Select((v, idx) => Tuple.Create(idx, v.Count)).MaxItems(x => x.Item2).SampleRandom(random).Item1;
var i = groups[max][0];
for (var k = 1; k < groups[max].Count; k++)
{
child[i] = groups[max][k];
remaining.Remove(i);
i = child[i];
}
child[i] = i;
remaining.Remove(i);
} while (remaining.Count > 0);
return(child);
}
