I was playing with a web simulator called evolution where you draw an imaginary creature (bones, joints, muscles) and then set it to the task of learning something like walking using a combination of a neural network plus genetic fitness selection.

One of the parameters you can tune is the length of the simulation for each generation before the individuals of the population are scored for fitness and culled as appropriate.

One particularly successful variant learned to (sort of) walk and then right before the end of the simulation timer it would fall forward. Because of the way the simulation measured fitness, it was always judged to have the greatest distance from the starting point and therefore would always survive to the next generation even though it wasn't the best walker.