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Breeding Race Cars With Genetic Algorithms

Posted by timothy on from the inbreeding-isn't-just-for-the-cheap-seats dept.

smack-pot writes "Wired News has an article about how the Digital Biology Interest Group at University College, London is using genetic algorithms to breed superfast Formula-One race cars. 68 design parameters were configurable in the cars, and the generated designs were tested using the racing simulation software developed by the game developer Electronic Arts. According to the research it is possible to shave off 88/100th of a second per lap by using genetic algorithms to tune the cars. In an industry where a tiny fraction of a second matters, that's significant."

2 of 187 comments (clear)

  1. Difference between simulation and reality by Minimind · · Score: 5, Insightful
    There is a large difference in evolved behaviour between physical things and models of those same things. GAs using physics simulators are very good at exploiting inaccuracies and subtle features of the simulation, making the transfer between the simulation to reality very difficult without the use of specialised techniques such as Minimal Simulations and Incremental Evolution.

    This means you have to be skeptical with experiments performed just in simulation without testing the same model in reality.

  2. what it shows ... by curator_thew · · Score: 5, Insightful


    Is that genetic algorithms are nice for parametric optimisation, but not for breakthrough innovation.