This is related to the "Verified Software" item in NewScientist's list of ideas that will change science. At the link below you'll find the text of the patents being auctioned: http://icapoceantomo.com/item-for-sale/exclusive-license-related-improved-methodology-formally-developing-control-systems :) Patent #7,627,538 ("Swarm autonomic agents with self-destruct capability") makes for quite an interesting read: "This invention relates generally to artificial intelligence and, more particularly, to architecture for collective interactions between autonomous entities." "In some embodiments, an evolvable synthetic neural system is operably coupled to one or more evolvable synthetic neural systems in a hierarchy." "In yet another aspect, an autonomous nanotechnology swarm may comprise a plurality of workers composed of self-similar autonomic components that are arranged to perform individual tasks in furtherance of a desired objective." "In still yet another aspect, a process to construct an environment to satisfy increasingly demanding external requirements may include instantiating an embryonic evolvable neural interface and evolving the embryonic evolvable neural interface towards complex complete connectivity." "In some embodiments, NBF 500 also includes genetic algorithms (GA) 504 at each interface between autonomic components. The GAs 504 may modify the intra-ENI 202 to satisfy requirements of the SALs 502 during learning, task execution or impairment of other subsystems."

Following up on our coffee-time discussion, here's an Evolutionary Algorithm where you are the fitness function, and evolution is guided by your subjective artistic sense. Start from scratch, or pick an existing image in the database, and start evolving. At every generation, you are presented with the individuals/images in the population. Pick the ones you like. Those will be the parents from which the next generation will be bred. Repeat, repeat... where do you get to? If you want to learn more about the science behind this, check the tutorial below by Kenneth Stanley, who is also this site's supervisor: http://dx.doi.org/10.1145/1830761.1830920

At the link below you find additional information about the projects: Education: Ten weeks to save the world http://www.nature.com/news/2010/100915/full/467266a.html

Link to the paper that is the subject of this article: Olsson-Francis, Karen and Cockell, Charles (2010). Use of cyanobacteria for in-situ resource use in space applications. Planetary And Space Science, 58(10), 1279-1285.http://dx.doi.org/10.1016/j.pss.2010.05.005

A very simple, but very creative application of evolution! Try it with your images. You can read about the first implementation of this kind here: http://developers.slashdot.org/developers/08/12/09/0238252.shtml

"We fit data to zonal interaction models and characterize which individual interaction forces suffice to explain observed spatial patterns." You can get the paper from the first author's website: http://people.stfx.ca/rlukeman/research.htm

Here they assume all relevant variables influencing behaviour are being observed. Namely, the relative positions and orientations of all ducks in the swarm. So, they make movies of the swarm's movements, process them, and them fit the models to that data. In the roots, though we can observe the complete final structure, or even obtain time-lapse movies showing how that structure came out to be, getting the measurements of all relevant soil variables (nitrogen, phosphorus, ...) throughout the soil, and over time, would be extremely difficult. So I guess a replication of the kind of work they did, but for the roots, would be hard. Nice reference though.