... continuing on from Genetic Programming/Genes
The mutation genetic operation is used fairly sparingly in the breeding process and works by chosing a node at random then replacing the tree below it by new randomly generated code. It's pretty straightforeward:
function gene mutate( gene g1) { // chose a node at random and replace with new random subtree local int numnodes, nodenum,i; local gpnode root1,target; local gene result; Local string s1; initfitstats(result); // grow tree from gene root1=spawn(class'gpnode'); root1.ReadFromString(g1.code); // find out how many nodes are in the tree we're mutating root1.countnodes(numnodes); // pick one at random nodenum=rand(numnodes-1)+2; // get reference to target node target=root1.findnode(nodenum); // clear subtree below target node target.prune(); // grow new subtree target.RandomGrow(target.depth,maxdepth); // write out new tree root1.WriteToString(result.code); // clean up root1.prune(); root1.Destroy(); return(result); }
So, having coded up routines to mangle GP trees in various ways there's a need for something to actually organise the planned orgy of interbreeding. Sorting the array in order of performance sounds like a good idea to start with so why not break out a good old bubblesort, I know there are quicker ways but this only gets done fairly rarely and our array isn't that huge so what the heck:
function sortgenes() { local int i,j; for(i=0;i<599;i++) for(j=0;j<(599-i);J++) if(eval(pool[i+1]) > eval(pool[i])) swap(i,i+1); } function float eval( gene g1) { // evaluate fitness of gene // submitted. // define in subclass local float result; local float playedVhumans, playedVbots; playedVhumans = g1.stats.GameswonVhumans + g1.stats.GameslostVhumans; playedvbots = g1.stats.gameswonVbots + g1.stats.gameslostVbots; if( playedVhumans ==0 ) playedVHumans=1; // to avoid divide by zeros if( playedVbots ==0) playedVbots=1; result = (g1.stats.gameswonVbots / playedvbots) + 2 * (g1.stats.gameswonVhumans / playedVhumans) ; return(result); } function swap( int idx1, int idx2) { // swap over two genes in pool // define in subclass local gene temp; temp=pool[idx1]; pool[idx1]=pool[idx2]; pool[idx2]=temp; pool[idx1].PoolIdx=idx1; pool[idx2].PoolIdx=idx2; }
That takes care if the sorting, now to code up the routine that makes actual *evolution* happen, so far all we've been doing is randomising everything, now we select our stud stock and breed the next generation of AI... it's a nested loop fest, it's:
function breednewpool() { local gene breeders[16]; local int i,j; // take the top performers and breed a new generation from them // first sort them in order of performance sortgenes(); // keep top fifteen performers for(i=0;i<16;i++) breeders[i]=pool[i]; // breed (crossover) the top sixteen against each other in all perms... twice for(i=0;i<16;i++) for(j=0;j<16;j+=2) crossover(breeders[i],breeders[j],pool[16+j+i*16],pool[j+17+i*16]); for(i=0;i<15;i++) for(j=0;j<15;j+=2) crossover(breeders[i],breeders[j],pool[272+j+i*16],pool[273+j+i*16]); // throw in a few mutations each too for(i=1;i<16;i++) { pool[512+i]=mutate(breeders[i]); pool[528+i]=mutate(breeders[i]); pool[544+i]=mutate(breeders[i]); pool[560+i]=mutate(breeders[i]); } // that makes 576 by my reckoning... blank the code of the lowest performers so they // get new random code next time for(i=576;i<600;i++) { pool[i].code=""; initfitstats(pool[i]); } generations ++; saveconfig(); }
... to be continued ...
Foxpaw: I didn't see the crossover function listed, I'm curious as to how it works on that tree. Have you written it yet?
Zedsquared: Yep, it's back a page on Genetic Programming/Genes I hope to be getting back in the saddle with this stuff soon, watch this space :)