@inproceedings{775f4961616240c7be675e190cbe0e8a,
title = "Nonlinear dynamics modelling for controller evolution",
abstract = "The problem of how to acquire a model of a physical robot,which is fit for evolution of controllers that can subsequently be used to control that robot, is considered in the context of racing a radio-controlled toy car around a randomised track. Several modelling techniques are compared, and the specific properties of the acquired models that influence the quality of the evolved controller are discussed. As we aim tominimise the amount of domain knowledge used, we furtherinvestigate the relation between the assumptions about the modelled system made by particular modelling techniques and the suitability of the acquired models as bases for controller evolution. We find that none of the models acquired is good enough on its own, and that a key to evolving robustbehaviour is to evaluate controllers simultaneously on multiple models during evolution. Examples of successfully evolved racing control for the physical car are analysed.",
keywords = "Evolutionary robotics, Forward models, Games, Neural networks, System identification",
author = "Julian Togelius and {De Nardi}, Renzo and Hugo Marques and Richard Newcombe and Lucas, {Simon M.} and Owen Holland",
year = "2007",
doi = "10.1145/1276958.1277020",
language = "English (US)",
isbn = "1595936971",
series = "Proceedings of GECCO 2007: Genetic and Evolutionary Computation Conference",
pages = "324--333",
booktitle = "Proceedings of GECCO 2007",
note = "9th Annual Genetic and Evolutionary Computation Conference, GECCO 2007 ; Conference date: 07-07-2007 Through 11-07-2007",
}