Breeding fast cars

University College London researchers have developed a computer model based on the use of genetic algorithms that ‘breeds’ winning Formula One cars.

Speed is the name of the game in the world of racing and now UCL scientists have developed a technique that ‘breeds’ winning Formula One cars.

Specifically, the developers have built a computer model based on genetic algorithms that optimises performance by selectively combining the best settings of Formula One cars to produce the ultimate configuration.

And they have demonstrated in simulations that it’s possible to knock crucial tenths of a second off lap time by tailoring a car’s setup to whatever conditions are faced on the track.

“Formula One spends millions each year designing and applying the latest technology to ensure their cars can handle whatever is thrown at them on the track. Each car can be modified in hundreds of way to optimise performance. Even minor changes in wing height, suspension stiffness or type of tyre rubber are ‘tweaked’ to give them the competitive edge,” says Dr. Peter Bentley, leader of the Digital Biology Group at UCL’s Department of Computer Science.

“Before every race, attempts are made to optimise settings for given conditions but cars are so finely calibrated than even subtle changes in temperature can affect performance. Decisions are based on experience but there are no guarantees they will always get it right,” he added.

“By running simulations we were able to distinguish how different facets of the car perform. Each best performance solution was treated as though it had its own genes that define those parameters. These winning solutions were then bred to produce the next generation, which combined the best settings of both parent cars until eventually we evolved the ultimate Formula One vehicle setup,” he says.

Genetic algorithms are an emerging technology that unites the fields of biology and computer science by mimicking the process of evolution in computers in an effort to find the best solutions to complex problems. A number of possible solutions to the problem are treated as ‘organisms’ known as phenotypes.

These are placed into a simulated environment, allowing them to be judged by a set of conditions. Only the better phenotypes survive and they produce ‘children’ in the next generation. These children are then judged in the environment, the better ones have children, and so on. After a number of generations have passed, fitter phenotypes evolve with new forms better suited to the task required.

“The real test would be to use our system in an actual Formula One car,” says Dr. Bentley. “At present, they have their own software that monitors performance during a race. Using our system you could evolve the car setup while the racing is going on. So if a car was damaged, at the next pit stop you could optimise the settings to offset whatever has gone wrong. You could even beam changes to the car while it is on the track, but somehow I don’t think racing authorities would go for that.”

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