Master Baboon The sea of the simulation


Evolve a Dune Buggy

Natural selection is the algorithm Nature uses to maximize the probability of reproduction of organisms. With Genetic Algorithms (GAs), engineers imitate this process in order to optimize a set of parameters in an engineering problem... or a dune buggy! The great application at uses simulated physics to evolve 2D cars that are optimally fast and stable.


Example of an evolved car.

GAs tie the ability to solve a problem to the likelihood of reproduction of a set of parameters: first, one creates a population of possible solutions to a problem, evaluates the solutions, and then forms a new generation by allowing the most successful ones to pass their parameters to the next generation, after small mutations and parameters swapping.

My general feeling about GAs is that in most cases other optimization algorithms will give similar or better results with less evaluations of the fitness function (which is typically the bottleneck). In Chapter 30.2 of his classic book on information theory, David McKay gives a very interesting interpretation of GAs as a Monte Carlo sampling in the space of parameters, and discusses the relation with efficient sampling methods, of which we have a better formal understanding.

Facebook Twitter Email
Comments (0) Trackbacks (0)

No comments yet.

Leave a comment

No trackbacks yet.