5 February 2009

Hop, skip and a jump

Christopher MacLeod and colleagues at the Robert Gordon University in Aberdeen have taken the idea of evolutionary algorithm to "evolve" an optimal control system for a robot a step further, and developed an incremental evolutionary algorithm (IEA) capable of adding new parts to the robot brain over time:
The team started with a simple robot the size of a paperback book, with two rotatable pegs for legs that could be turned by motors through 180 degrees. They then gave the robot's six-neuron control system its primary command - to travel as far as possible in 1000 seconds. The software then set to work evolving the fastest form of locomotion to fulfil this task.

"It fell over mostly, in a puppyish kind of way," says MacLeod. "But then it started moving forward and not falling over straight away - and then it got better and better until it could eventually hop along the bench like a mudskipper."
-- Unnatural selection: Robots start to evolve

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