{"_id":"19980","title":"The Effect of Fitness Function Design on Performance in Evolutionary Robotics: The Influence of a Priori Knowledge","department":[{"_id":"63"},{"_id":"238"}],"user_id":"15415","citation":{"ieee":"H. Hamann and M. Divband Soorati, “The Effect of Fitness Function Design on Performance in Evolutionary Robotics: The Influence of a Priori Knowledge,” in <i>Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2015)</i>, 2015, pp. 153–160.","ama":"Hamann H, Divband Soorati M. The Effect of Fitness Function Design on Performance in Evolutionary Robotics: The Influence of a Priori Knowledge. In: <i>Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2015)</i>. ACM; 2015:153-160. doi:<a href=\"https://doi.org/10.1145/2739480.2754676\">10.1145/2739480.2754676</a>","mla":"Hamann, Heiko, and Mohammad Divband Soorati. “The Effect of Fitness Function Design on Performance in Evolutionary Robotics: The Influence of a Priori Knowledge.” <i>Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2015)</i>, ACM, 2015, pp. 153–60, doi:<a href=\"https://doi.org/10.1145/2739480.2754676\">10.1145/2739480.2754676</a>.","bibtex":"@inproceedings{Hamann_Divband Soorati_2015, title={The Effect of Fitness Function Design on Performance in Evolutionary Robotics: The Influence of a Priori Knowledge}, DOI={<a href=\"https://doi.org/10.1145/2739480.2754676\">10.1145/2739480.2754676</a>}, booktitle={Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2015)}, publisher={ACM}, author={Hamann, Heiko and Divband Soorati, Mohammad}, year={2015}, pages={153–160} }","apa":"Hamann, H., &#38; Divband Soorati, M. (2015). The Effect of Fitness Function Design on Performance in Evolutionary Robotics: The Influence of a Priori Knowledge. In <i>Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2015)</i> (pp. 153–160). ACM. <a href=\"https://doi.org/10.1145/2739480.2754676\">https://doi.org/10.1145/2739480.2754676</a>","short":"H. Hamann, M. Divband Soorati, in: Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2015), ACM, 2015, pp. 153–160.","chicago":"Hamann, Heiko, and Mohammad Divband Soorati. “The Effect of Fitness Function Design on Performance in Evolutionary Robotics: The Influence of a Priori Knowledge.” In <i>Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2015)</i>, 153–60. ACM, 2015. <a href=\"https://doi.org/10.1145/2739480.2754676\">https://doi.org/10.1145/2739480.2754676</a>."},"date_created":"2020-10-12T13:12:25Z","status":"public","year":"2015","publisher":"ACM","author":[{"first_name":"Heiko","full_name":"Hamann, Heiko","last_name":"Hamann"},{"full_name":"Divband Soorati, Mohammad","last_name":"Divband Soorati","first_name":"Mohammad"}],"abstract":[{"lang":"eng","text":"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."}],"doi":"10.1145/2739480.2754676","page":"153-160","type":"conference","date_updated":"2022-01-06T06:54:17Z","publication":"Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2015)","language":[{"iso":"eng"}]}