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]";
	
	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--)
			Simulator.step(); // simulate 'expected lifespan' steps after which 'best' may have changed. This helps avoid too frequent calls to best()
		best=GenePools[0].best();
	}
	Simulator.stop();
	Simulator.print("%g (x%g) %s" % best.fit % best.popsiz % best.genotype);
}
~