Can evolutionary theory help rival Formula 1 teams break Michael Schumacher's seemingly unassailable hold on top-flight motor racing? Computer scientists at University College London think it might.
Peter Bentley and Krzysztof Wloch have used genetic algorithms software that mimics evolution's drive for fitness to breed the best tuning configurations for racing cars.
Using the technique, they shaved a second off the best time achieved by an expert. They will present their results at a conference on evolutionary systems in Seattle next week.
Genetic algorithms mimic the principles of evolution to breed solutions to a problem. A population of potential solutions is tested for fitness and the best are cross-bred and mutated. The unfit members of the next generation are weeded out, simulating natural selection, leaving the fittest solutions to go on to breed.
Unfortunately Bentley and Wloch did not have any real cars to hand, so instead they applied their algorithm to virtual cars in the PC game Formula One Challenge.
This lets players set 68 variables governing the car's performance, including factors such as rev limits, gear ratios, tyre pressures and suspension damping. They say there is no reason why the same principle could not be applied trackside at Formula 1 races.
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