I. Harvey, P. Husbands, D. Cliff
We analyse how the project of evolving 'neural' network controller for autonomous visually guided robots is significantly different from the usual function optimisation problems standard genetic algorithms are asked to tackle. The need to have open ended increase in complexity of the controllers, to allow for an indefinite number of new tasks to be incrementally added to the robot's capabilities in the long term, means that gentotypes of arbitrary length need to be allowed. This results in populations being genetically converged as new tasks are added, and needs a change to usual genetic algorithm practices. Results of successful runs are shown, and the population is analysed in terms of genetic convergence and movement in time across sequence space.
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