Publikation: The Effect of Fitness Function Design on Performance in Evolutionary Robotics : The Influence of a Priori Knowledge
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Fitness function design is known to be a critical feature of the evolutionary-robotics approach. Potentially, the complexity of evolving a successful controller for a given task can be reduced by integrating a priori knowledge into the fitness function which complicates the comparability of studies in evolutionary robotics. Still, there are only few publications that study the actual effects of different fitness functions on the robot's performance. In this paper, we follow the fitness function classification of Nelson et al. (2009) and investigate a selection of four classes of fitness functions that require different degrees of a priori knowledge. The robot controllers are evolved in simulation using NEAT and we investigate different tasks including obstacle avoidance and (periodic) goal homing. The best evolved controllers were then post-evaluated by examining their potential for adaptation, determining their convergence rates, and using cross-comparisons based on the different fitness function classes. The results confirm that the integration of more a priori knowledge can simplify a task and show that more attention should be paid to fitness function classes when comparing different studies.
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DIVBAND SOORATI, Mohammad, Heiko HAMANN, 2015. The Effect of Fitness Function Design on Performance in Evolutionary Robotics : The Influence of a Priori Knowledge. GECCO '15 : Annual Conference on Genetic and Evolutionary Computation. Madrid, Spain, 11. Juli 2015 - 15. Juli 2015. In: SILVA, Sara, ed. and others. GECCO Companion '15 : Proceedings of the Companion Publication of the 2015 Annual Conference on Genetic and Evolutionary Computation. New York, NY: ACM, 2015, pp. 153-160. ISBN 978-1-4503-3472-3. Available under: doi: 10.1145/2739480.2754676BibTex
@inproceedings{DivbandSoorati2015Effec-59891, year={2015}, doi={10.1145/2739480.2754676}, title={The Effect of Fitness Function Design on Performance in Evolutionary Robotics : The Influence of a Priori Knowledge}, isbn={978-1-4503-3472-3}, publisher={ACM}, address={New York, NY}, booktitle={GECCO Companion '15 : Proceedings of the Companion Publication of the 2015 Annual Conference on Genetic and Evolutionary Computation}, pages={153--160}, editor={Silva, Sara}, author={Divband Soorati, Mohammad and Hamann, Heiko} }
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