script:
name:Evolve for speed vs gravity
help:Evolve for speed in different gravity settings
code:~
function main_args(gravity,min_evaluations)
{
	Math.randomize();
	World.wrldg=gravity;
	Populations[0].perfperiod=100000; //fitness: velocity serves as distance (because sampling period is longer than lifespan)
	ExpProperties.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="""
 function penalty(count)
 {
  var MAX_WITHOUT_PENALTY=50;
  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 (due to stagnation detected)
	
	var best=GenePools[0].best();
	Simulator.print("Optimization ends, best: %g (x%g) %s" % best.fit % best.instances % best.genotype);
	
	// Now, since we have indeterminism (default settings 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 keep evaluating until the single best genotype is evaluated at least min_evaluations times.
	// No new genotypes are introduced in this phase.
	UserScripts.script_Find_best_genotype_on_average_args(min_evaluations,"best_%g_%u.expt" % gravity % int(Math.time));
}
~