| 1 | script:
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| 2 | name:Evolve for speed vs gravity
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| 3 | help:Evolve for speed in different gravity settings
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| 4 | code:~
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| 5 | function main(gravity,min_evaluations)
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| 6 | {
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| 7 | Math.randomize();
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| 8 | World.wrldg=gravity;
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| 9 | Populations[0].perfperiod=100000; //fitness: velocity serves as distance (because sampling period is longer than lifespan)
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| 10 | ExpParams.initialgen="XX[|,1:1][N,1:1,2:1][T][G]";
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| 11 |
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| 12 | //custom fitness function: velocity minus small penalty for complexity (high number of parts, joints, neurons, connections)
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| 13 | GenePools[0].fitness="""
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| 14 | var MAX_WITHOUT_PENALTY=50;
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| 15 | function penalty(count)
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| 16 | {
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| 17 | var toomany=count-MAX_WITHOUT_PENALTY;
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| 18 | if (toomany<=0) return 0; else return -toomany*0.001;
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| 19 | }
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| 20 | return this.velocity+penalty(this.numparts)+penalty(this.numjoints)+penalty(this.numneurons)+penalty(this.numconnections);""";
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| 21 |
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| 22 | Simulator.init();
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| 23 | //Simulator.print(GenePools[0][0].genotype); //ensure the initialgen is in the gene pool
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| 24 |
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| 25 | Simulator.start();
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| 26 | while (Simulator.running) Simulator.step(); //runs until the experiment stops by itself
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| 27 | var best=GenePools[0].best();
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| 28 | Simulator.print("%g (x%g) %s" % best.fit % best.popsiz % best.genotype);
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| 29 |
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| 30 | // Now, since we have indeterminism (default.sim used: random initialization of neural states and random placement of creatures),
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| 31 | // we cannot trust fitness values that have not been confirmed (averaged) during multiple evaluations.
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| 32 | // So we start another phase where we wait until the best genotype is evaluated at least min_evaluations times.
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| 33 | // No new genotypes are introduced in this phase.
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| 34 | ExpParams.stagnation=0; //turn off stagnation detection mechanism
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| 35 | ExpParams.p_mut=0; //we don't want evolution and new genotypes anymore. We only want to evaluate existing genotypes multiple times
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| 36 | ExpParams.p_xov=0;
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| 37 | Simulator.start();
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| 38 | while (Simulator.running && best.popsiz<min_evaluations) //repeat until the best genotype will be evaluated at least min_evaluations times
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| 39 | {
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| 40 | 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()
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| 41 | Simulator.step();
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| 42 | best=GenePools[0].best();
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| 43 | }
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| 44 | Simulator.stop();
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| 45 | Simulator.print("%g (x%g) %s" % best.fit % best.popsiz % best.genotype);
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| 46 |
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| 47 | GenePools[0].clear(); //remove all...
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| 48 | best.moveTo(GenePools[0]); //...then restore best...
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| 49 | 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
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| 50 | }
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| 51 | ~
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| 52 |
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