The guys working on EA's Spore considered evolving their creature animations, but opted instead for retargeted animation generated by hand. http://forum.spore.com/jforum/posts/list/28338.page

There are also procedural animation techniques, where you have a fixed skeleton and you are trying to find an animation that satisfies user-specified constraints, but that is really more of a motion-control topic.

I recall a professor of mine saying that Karl Sims's project is a popular choice for a final project in his advanced graphics course, but he tries to discourage it, as it turns out to be deceptively difficult to implement in a semester, even for particularly capable students.

http://www.genetic-programming.com/jkpubs2004.html

But in general, sims' results are still among the most impressive ALife demonstrations.

I've seen the video before and it's kind of cool, can't find the link now though. Anyone?

"Creatures can learn a simple verb-object language, either via keyboard input from the user, or by playing on a teaching-machine in the environment, or from interactions with other creatures in the environment." http://www.cyberlife-research.com/cyberliferesearch/Creature...

The most impressive use of simulated evolution that I've seen recently was a paper which describes the evolution of proteins, how this evolution mirrors a simulated annealing algorithm how this behaviour forms species.

http://www.pnas.org/content/106/6/1869.abstract

For genetic programming in general there was a paper in Science this year which is beautifully written and describes how physical laws appeared in the output of a genetic program applied to experimental data:

http://ccsl.mae.cornell.edu/natural_laws

If anybody is interested in genetic programming the following free book is very practical:

http://www.gp-field-guide.org.uk/

Before we did the full-on evolutionary stuff, I was fiddling with simulated annealing. There's definitely a connection.

It's all a subset of global optimization really. What makes evolution so fascinating is how it seems (at least in real life) to be able to direct its own path towards interesting solutions to particular problems. It's sort of like this mad genius, who keeps shouting brilliant things at you, but never listens. So you can never ask him a question -- you just have to set things up so he'll want to find an answer to the question you wish you could ask.

http://devolab.msu.edu

Basically, the idea is this: Typically in GAs, the genotype (the bits that make up the genome, the ones that undergo selection and recombination) maps directly to a phenotype (one to one).

So if you were going to evolve a brain, there would have to be one gene for each connection in the brain. The problem is, such a direct 1-1 encoding can't scale to huge brains. For example in the human brain there are trillions of connections (synapses), which would mean searching a trillion-dimensional space (unfathomably difficult). Nature's solution is to use development, where a brain grows over time, where the genes don't directly specify each connection, but the blueprint for how a brain should grow.

So, in generative encodings in GAs, the principle is simlar. An indirect (or generative) encoding is where one gene can represent an aspect of a developmental process instead of directly specifying each component. In some ways it's similar to how in engineering you reuse components when making a building; if the manufacture of each nail had to be specified completely separately a project would be overwhelming.

I haven't heard of generative encoding before -- it sounds really cool. I could imagine something similar done with Genetic Programming. You could design a DSL (domain specific language) that is capable of building Neural Nets and then perform GP on programs written in the DSL. This could take the pressure off having to design a good development algorithm -- the GP would would do that for you.

http://arxiv.org/abs/0907.1839

I've been fascinated by this idea since before I saw Karl Sim's work. My take on it now is that it is something that could really break out, but there needs to be some hard-ass work on scaling, and on how to apply the technique effectively. The hardware we have now is so off the charts compared to 1994, you would suppose that everything would be easier -- but not really. Evolution is one application that can really, really eat up every ounce of CPU you throw at it, and still scream for more.

But flops aren't the sticking point, at least not yet. What is extremely difficult is finding ways to map evolutionary techniques to real-world problems effectively. It's just such a radical way of thinking that it tends to work at cross-purposes to the 'normal' patterns of engineering.

I can elaborate if anyone cares.

All evolutionary approaches seem pretty susceptible to local optima when tackling ambitious problems like biped gaits. You maintain diversity through demes and use incremental evolution?

One thing I hate about research in CS is that rarely is the source code provided to reproduce the research. I spent 3 months unable to reproduce the seminal Reil + Husbands biped walking paper (Reil went on to found NaturalMotion, which makes natural motion for CGI in movies). I swear they used some sort of voodoo magic. Is the source available for your simulator?

Ventrella's main page is worth a look http://www.ventrella.com if you're interested in examples of genetic programing.