Some other evo models
Polyworld: http://www.beanblossom.in.us/larryy/PolyWorld.html. This simulation looks interesting, but I haven’t had time to explore it in any detail. From the home page:
PolyWorld is a computational ecology that I developed to explore issues in Artificial Life. Simulated organisms reproduce sexually, fight and kill and eat each other, eat the food that grows throughout the world, and either develop successful strategies for survival or die. An organism's entire behavioral suite (move, turn, attack, eat, mate, light) is controlled by its neural network "brain". Each brain's architecture--it's neural wiring diagram--is determined from its genetic code, in terms of number, size, and composition of neural clusters (excitatory and inhibitory neurons) and the types of connections between those clusters (connection density and topological mapping). Synaptic efficacy is modulated via Hebbian learning, so, in principle, the organisms have the ability to learn during the course of their lifetimes. The organisms perceive their world through a sense of vision, provided by a computer graphic rendering of the world from each organism's point of view. The organisms' physiologies are also encoded genetically, so both brain and body, and thus all components of behavior, evolve over multiple generations. A variety of "species", with varying individual and group survival strategies have emerged in various simulations, displaying such complex ethological behaviors as swarming/flocking, foraging, and attack avoidance.
Framsticks: http://www.frams.alife.pl/index.html. Another potentially interesting package that I haven’t had time to explore yet.
Nanopond: http://www.greythumb.org/wiki/Nanopond
This is perhaps the tightest evo simulator out there, on the order of 300 lines of code. It allows some control of interesting variables, but because of the huge default (and unchangeable) world size is painfully slow. It also provides a minimum of interesting data output.
Antworld: http://home.freeuk.com/ben.blundell/
This is an interesting simulation that has artificial agents (“ants”) equipped with sensors, motor capabilities, and neural nets. It was written as an undergraduate thesis project. Unfortunately, the author (now a grad student in CS) implemented only a sort of group selection model, and thus it is unsuitable for studying most interesting questions in evolution.