Just another update

Task/Evolutionary-algorithm/Aime/evolutionary-algorithm.aime

@@ -8,7 +8,7 @@ fitness(data t, data b)
     i = b_length(t);
     while (i) {
         i -= 1;
-        f += sign(b_character(t, i) ^ b_character(b, i));
+        f += sign(t[i] ^ b[i]);
     }
 
     return f;
@@ -23,9 +23,9 @@ mutate(data c, data b, data u)
     i = 0;
     while (i < l) {
         if (drand(15)) {
-            b_append(c, b_character(b, i));
+            b_append(c, b[i]);
         } else {
-            b_append(c, b_character(u, drand(26)));
+            b_append(c, u[drand(26)]);
         }
         i += 1;
     }
@@ -45,7 +45,7 @@ main(void)
     i = l;
     while (i) {
         i -= 1;
-        b_append(b, b_character(u, drand(26)));
+        b_append(b, u[drand(26)]);
     }
 
     f = fitness(t, b);
@@ -53,9 +53,7 @@ main(void)
         data n;
         integer a;
 
-        o_winteger(-4, f);
-        o_text(b_string(b));
-        o_newline();
+        o_form("/lw4/~\n", f, b_string(b));
 
         n = b;
 
@@ -75,9 +73,7 @@ main(void)
         b = n;
     }
 
-    o_winteger(-4, f);
-    o_text(b_string(b));
-    o_newline();
+    o_form("/lw4/~\n", f, b_string(b));
 
     return 0;
 }

Task/Evolutionary-algorithm/ColdFusion/evolutionary-algorithm.cfm

@@ -0,0 +1,67 @@
+<Cfset theString = 'METHINKS IT IS LIKE A WEASEL'>
+<cfparam name="parent" default="">
+<Cfset theAlphabet = "ABCDEFGHIJKLMNOPQRSTUVWXYZ ">
+<Cfset fitness = 0>
+<Cfset children = 3>
+<Cfset counter = 0>
+
+<Cfloop from="1" to="#children#" index="child">
+  <Cfparam name="child#child#" default="">
+  <Cfparam name="fitness#child#" default=0>
+</Cfloop>
+
+<Cfloop condition="fitness lt 1">
+
+  <Cfset oldparent = parent>
+  <Cfset counter = counter + 1>
+
+  <cfloop from="1" to="#children#" index="child">
+    <Cfset thischild = ''>
+
+    <Cfloop from="1" to="#len(theString)#" index="i">
+      <cfset Mutate = Mid(theAlphabet, RandRange(1, 28), 1)>
+      <cfif fitness eq 0>
+        <Cfset thischild = thischild & mutate>
+      <Cfelse>
+
+        <Cfif Mid(theString, i, 1) eq Mid(variables["child" & child], i, 1)>
+          <Cfset thischild = thischild & Mid(variables["child" & child], i, 1)>
+        <Cfelse>
+          <cfset MutateChance = 1/fitness>
+          <Cfset MutateChanceRand = rand()>
+          <Cfif MutateChanceRand lte MutateChance>
+            <Cfset thischild = thischild & mutate>
+          <Cfelse>
+            <Cfset thischild = thischild & Mid(variables["child" & child], i, 1)>
+          </Cfif>
+        </Cfif>
+
+      </cfif>
+    </Cfloop>
+
+    <Cfset variables["child" & child] = thischild>
+
+</cfloop>
+
+  <cfloop from="1" to="#children#" index="child">
+    <Cfset thisChildFitness = 0>
+    <Cfloop from="1" to="#len(theString)#" index="i">
+      <Cfif Mid(variables["child" & child], i, 1) eq Mid(theString, i, 1)>
+        <Cfset thisChildFitness = thisChildFitness + 1>
+      </Cfif>
+    </Cfloop>
+
+    <Cfset variables["fitness" & child] = (thisChildFitness)/len(theString)>
+
+    <Cfif variables["fitness" & child] gt fitness>
+      <Cfset fitness = variables["fitness" & child]>
+      <Cfset parent = variables["child" & child]>
+    </Cfif>
+
+  </cfloop>
+
+  <Cfif parent neq oldparent>
+    <Cfoutput>###counter# #numberformat(fitness*100, 99)#% fit: #parent#<br></Cfoutput><cfflush>
+  </Cfif>
+
+</Cfloop>

Task/Evolutionary-algorithm/D/evolutionary-algorithm.d

@@ -5,7 +5,7 @@ enum C = 100;  // Number of children in each generation.
 enum P = 0.05; // Mutation probability.
 enum fitness = (dchar[] s) => target.zip(s).count!q{ a[0] != a[1] };
 dchar rnd() { return (uppercase ~ " ")[uniform(0, $)]; }
-enum mut= (dchar[] s) => s.map!(a=> uniform(0,1.) < P ? rnd : a).array;
+enum mut = (dchar[] s) => s.map!(a => uniform01 < P ? rnd : a).array;
 
 void main() {
     auto parent = target.length.iota.map!(_ => rnd).array;

Task/Evolutionary-algorithm/Octave/evolutionary-algorithm.octave

@@ -2,27 +2,26 @@ global target;
 target = split("METHINKS IT IS LIKE A WEASEL", "");
 charset = ["A":"Z", " "];
 p = ones(length(charset), 1) ./ length(charset);
-parent = discrete_rnd(length(target), charset, p)';
-mutaterate = 0.01;
+parent = discrete_rnd(charset, p, length(target), 1);
+mutaterate = 0.1;
 
-C = 100;
+C = 1000;
 
-function r = fitness(parent, thetarget)
-  r = sum(parent == thetarget) ./ length(thetarget);
+function r = fitness(parent, target)
+  r = sum(parent == target) ./ length(target);
 endfunction
 
-function r = mutate(parent, therate, charset)
+function r = mutate(parent, mutaterate, charset)
   r = parent;
   p = unifrnd(0, 1, length(parent), 1);
-  nmutants = sum( p < therate );
+  nmutants = sum( p < mutaterate );
   if (nmutants)
-    s = discrete_rnd(nmutants, charset, ones(length(charset), 1) ./ length(charset))';
-    r( p < therate ) = s;
+    s = discrete_rnd(charset, ones(length(charset), 1) ./ length(charset),nmutants,1);
+    r( p < mutaterate ) = s;
   endif
 endfunction
 
-function r = evolve(parent, mutatefunc, fitnessfunc, C, mutaterate, \
-		    charset)
+function r = evolve(parent, mutatefunc, fitnessfunc, C, mutaterate, charset)
   global target;
   children = [];
   for i = 1:C
@@ -42,10 +41,11 @@ function printgen(p, t, i)
 endfunction
 
 i = 0;
+
 while( !all(parent == target) )
   i++;
   parent = evolve(parent, @mutate, @fitness, C, mutaterate, charset);
-  if ( mod(i, 20) == 0 )
+  if ( mod(i, 1) == 0 )
     printgen(parent, target, i);
   endif
 endwhile

Task/Evolutionary-algorithm/OoRexx/evolutionary-algorithm.rexx

@@ -0,0 +1,54 @@
+/* Weasel.rex - Me thinks thou art a weasel. - G,M.D. - 2/25/2011 */
+arg C M
+/* C is the number of children parent produces each generation. */
+/* M is the mutation rate of each gene (character) */
+
+call initialize
+generation = 0
+do until parent = target
+   most_fitness = fitness(parent)
+   most_fit     = parent
+   do C
+      child = mutate(parent, M)
+      child_fitness = fitness(child)
+      if child_fitness > most_fitness then
+      do
+         most_fitness = child_fitness
+         most_fit = child
+         say "Generation" generation": most fit='"most_fit"', fitness="left(most_fitness,4)
+      end
+   end
+   parent = most_fit
+   generation = generation + 1
+end
+exit
+
+initialize:
+   target   = "METHINKS IT IS LIKE A WEASEL"
+   alphabet = "ABCDEFGHIJKLMNOPQRSTUVWXYZ "
+   c_length_target = length(target)
+   parent  = mutate(copies(" ", c_length_target), 1.0)
+   do i = 1 to c_length_target
+      target_ch.i = substr(target,i,1)
+   end
+return
+
+fitness: procedure expose target_ch. c_length_target
+   arg parm_string
+   fitness = 0
+   do i_target = 1 to c_length_target
+      if substr(parm_string,i_target,1) = target_ch.i_target then
+         fitness = fitness + 1
+   end
+return fitness
+
+mutate:procedure expose alphabet
+arg string, parm_mutation_rate
+   result = ""
+   do istr = 1 to length(string)
+      if random(1,1000)/1000 <= parm_mutation_rate then
+         result = result || substr(alphabet,random(1,length(alphabet)),1)
+      else
+         result = result || substr(string,istr,1)
+   end
+return result

Task/Evolutionary-algorithm/Perl-6/evolutionary-algorithm.pl6

@@ -10,4 +10,4 @@ loop (
     my $parent = alphabet.roll(target.chars).join;
     $parent ne target;
     $parent = max :by(&fitness), mutate($parent) xx C
-) { printf "%6d: '%s'\n", (state $)++, $parent }
+) { printf "%6d: '%s'\n", $++, $parent }