source: experiments/frams/evolve-speed-vs-gravity/data/scripts/evolve-speed-vs-gravity.script @ 250

Last change on this file since 250 was 250, checked in by Maciej Komosinski, 10 years ago

Introduced penalty for too complex creatures and saves best genotypes in files for further examination

File size: 2.3 KB
2name:Evolve for speed vs gravity
3help:Evolve for speed in different gravity settings
5function main(gravity,min_evaluations)
7        Math.randomize();
8        World.wrldg=gravity;
9        Populations[0].perfperiod=100000; //fitness: velocity serves as distance (because sampling period is longer than lifespan)
10        ExpParams.initialgen="XX[|,1:1][N,1:1,2:1][T][G]";
11        GenePools[0].fitness="""function penalty(count)
12 {
13 var toomany=count-50;
14 if (toomany<=0) return 0; else return -toomany*0.001;
15 }
16 return this.velocity+penalty(this.numparts)+penalty(this.numjoints)+penalty(this.numneurons)+penalty(this.numconnections);""";
18        Simulator.init();
19        //Simulator.print(GenePools[0][0].genotype); //ensure the initialgen is in the gene pool
21        Simulator.start();
22        while (Simulator.running) Simulator.step(); //runs until the experiment stops by itself
23        var best=GenePools[0].best();
24        Simulator.print("%g (x%g) %s" % % best.popsiz % best.genotype);
26        // Now, since we have indeterminism (default.sim used: random initialization of neural states and random placement of creatures),
27        // we cannot trust fitness values that have not been confirmed (averaged) during multiple evaluations.
28        // So we start another phase where we wait until the best genotype is evaluated at least min_evaluations times.
29        // No new genotypes are introduced in this phase.
30        ExpParams.stagnation=0; //turn off stagnation detection mechanism
31        ExpParams.p_mut=0; //we don't want evolution and new genotypes anymore. We only want to evaluate existing genotypes multiple times
32        ExpParams.p_xov=0;
33        Simulator.start();
34        while (Simulator.running && best.popsiz<min_evaluations) //repeat until the best genotype will be evaluated at least min_evaluations times
35        {
36                for(var t=best.lifespan; t>0 && Simulator.running; t--)
37                        Simulator.step(); // simulate 'expected lifespan' steps after which 'best' may have changed. This helps avoid too frequent calls to best()
38                best=GenePools[0].best();
39        }
40        Simulator.stop();
41        Simulator.print("%g (x%g) %s" % % best.popsiz % best.genotype);
43        GenePools[0].clear(); //remove all...
44        best.moveTo(GenePools[0]); //...then restore best...
45"best_%g_%u.expt" % gravity % (0+Math.time)); //...and save it in a file just in case we want to see all of its data
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