Path integration is an important navigation strategy in many animal species. We use a genetic algorithm to evolve a novel neural model of path integration, based on input from cells that encode the heading of the agent in a manner comparable to the polarization-sensitive interneurons found in insects. The home vector is encoded as a population code across a circular array of cells that integrate this input. This code can be used to control return to the home position. We demonstrate the capabilities of the network under noisy conditions in simulation and on a robot.

his paper describes the simulated car racing competition that was arranged as part of the 2007 IEEE Congress on Evolutionary Computation. Both the game that was used as the domain for the competition, the controllers submitted as entries to the competition and its results are presented. With this paper, we hope to provide some insight into the efficacy of various computational intelligence methods on a well-defined game task, as well as an example of one way of running a competition. In the process, we provide a set of reference results for those who wish to use the simplerace game to benchmark their own algorithms. The paper is co-authored by the organizers and participants of the competition

No abstract

No abstract