| 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 | Simulator.init();
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| 13 | //Simulator.print(GenePools[0][0].genotype); //ensure the initialgen is in the gene pool
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| 14 |
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| 15 | Simulator.start();
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| 16 | while (Simulator.running) Simulator.step(); //runs until the experiment stops by itself
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| 17 | var best=GenePools[0].best();
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| 18 | Simulator.print("%g (x%g) %s" % best.fit % best.popsiz % best.genotype);
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| 19 |
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| 20 | // Now, since we have indeterminism (default.sim used: random initialization of neural states and random placement of creatures),
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| 21 | // we cannot trust fitness values that have not been confirmed (averaged) during multiple evaluations.
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| 22 | // So we start another phase where we wait until the best genotype is evaluated at least min_evaluations times.
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| 23 | // No new genotypes are introduced in this phase.
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| 24 | ExpParams.stagnation=0; //turn off stagnation detection mechanism
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| 25 | 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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| 26 | ExpParams.p_xov=0;
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| 27 | Simulator.start();
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| 28 | while (Simulator.running && best.popsiz<min_evaluations) //repeat until the best genotype will be evaluated at least min_evaluations times
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| 29 | {
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| 30 | for(var t=best.lifespan; t>0 && Simulator.running; t--)
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| 31 | Simulator.step(); // simulate 'expected lifespan' steps after which 'best' may have changed. This helps avoid too frequent calls to best()
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| 32 | best=GenePools[0].best();
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| 33 | }
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| 34 | Simulator.stop();
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| 35 | Simulator.print("%g (x%g) %s" % best.fit % best.popsiz % best.genotype);
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| 36 | }
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| 37 | ~
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| 38 |
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