source: experiments/frams/evolve-speed-vs-gravity/data/scripts/evolve-speed-vs-gravity.script @ 263

script:
name:Evolve for speed vs gravity
help:Evolve for speed in different gravity settings
code:~
function main(gravity,min_evaluations)
{
        Math.randomize();
        World.wrldg=gravity;
        Populations[0].perfperiod=100000; //fitness: velocity serves as distance (because sampling period is longer than lifespan)
        ExpParams.initialgen="XX[|,1:1][N,1:1,2:1][T][G]";
        
        //custom fitness function: velocity minus small penalty for complexity (high number of parts, joints, neurons, connections)
        GenePools[0].fitness="""
 var MAX_WITHOUT_PENALTY=50;
 function penalty(count)
 {
 var toomany=count-MAX_WITHOUT_PENALTY;
 if (toomany<=0) return 0; else return -toomany*0.001;
 }
 return this.velocity+penalty(this.numparts)+penalty(this.numjoints)+penalty(this.numneurons)+penalty(this.numconnections);""";

        Simulator.init();
        //Simulator.print(GenePools[0][0].genotype); //ensure the initialgen is in the gene pool
                
        Simulator.start();
        while (Simulator.running) Simulator.step(); //runs until the experiment stops by itself
        var best=GenePools[0].best();
        Simulator.print("%g (x%g) %s" % best.fit % best.popsiz % best.genotype);
        
        // Now, since we have indeterminism (default.sim used: random initialization of neural states and random placement of creatures),
        // we cannot trust fitness values that have not been confirmed (averaged) during multiple evaluations.
        // So we start another phase where we wait until the best genotype is evaluated at least min_evaluations times.
        // No new genotypes are introduced in this phase.
        ExpParams.stagnation=0; //turn off stagnation detection mechanism
        ExpParams.p_mut=0; //we don't want evolution and new genotypes anymore. We only want to evaluate existing genotypes multiple times
        ExpParams.p_xov=0;
        Simulator.start();
        while (Simulator.running && best.popsiz<min_evaluations) //repeat until the best genotype will be evaluated at least min_evaluations times
        {
                for(var t=best.lifespan; t>0 && Simulator.running; t--) // simulate 'expected lifespan' steps after which 'best' may have changed. This helps avoid frequent unnecessary calls to best()
                        Simulator.step();
                best=GenePools[0].best();
        }
        Simulator.stop();
        Simulator.print("%g (x%g) %s" % best.fit % best.popsiz % best.genotype);
        
        GenePools[0].clear(); //remove all...
        best.moveTo(GenePools[0]); //...then restore best...
        Simulator.save("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
}
~