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Convex crossover in genetic algorithm

• To: mathgroup at smc.vnet.net
• Subject: [mg112654] Convex crossover in genetic algorithm
• From: lightnation <lightnation at naver.com>
• Date: Fri, 24 Sep 2010 05:39:26 -0400 (EDT)

If you are interested in the genetic algorithm,
please help me make coding about convex crossover.
The coding below is convex crossover in RCGA.
It's working, but the convergence is not so good.
Please correct me.
(The whole algorithm is posted in this.
http://blog.daum.net/lightnation/5846116)

crossover[varvalue_List, pc_] :=
 Module[{length, lengthtemp, crosspool, temp1, temp2, tub, crosstub,
   xcrossprob, outtaxcrossindex, outtaelement, end1, end2, end,
   crosspooltemp, outtaelementtemp, temp},
  length = Length[varvalue];
  crosstub = {};
  tub = {};
  lengthtemp = Range[length];

  For[k = 1, k <= length/2,
   temp1 = RandomChoice[lengthtemp];
   lengthtemp = Complement[lengthtemp, {temp1}];
   temp2 = RandomChoice[lengthtemp];
   tub = {temp1, temp2};
   AppendTo[crosstub, tub];
   lengthtemp = Complement[lengthtemp, {temp2}];
   k++];

  crosspooltemp =
   Map[{varvalue[[#[[1]]]], varvalue[[#[[2]]]]} &, crosstub];
  xcrossprob = RandomReal[1, length/2];
  outtaxcrossindex =
   Flatten[Map[Position[xcrossprob, #] &,
     Select[xcrossprob, # > pc &]], 1];
  outtaelement = Map[crosspooltemp[[#[[1]]]] &, outtaxcrossindex];
  crosspool = Delete[crosspooltemp, outtaxcrossindex];
  temp = Map[{#[[1]], #[[2]],
      RandomReal[1, Length[crosspool[[1, 1]]]]} &, crosspool];

  end1 = Map[MapThread[(#1 (1 - #3) + #2 #3) &, #] &, temp];
  end2 = Map[MapThread[(#1 #3 + #2 (1 - #3)) &, #] &, temp];

  end = Join[end1, end2, Flatten[outtaelement, 1]];

  Return[end];
  ];