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Silva, F., Correia, L. & Christensen, A. L. (2016). Online hyper-evolution of controllers in multirobot systems. In Cabri, G., Picard, G., and Suri, N. (Ed.), 2016 IEEE 10th International Conference on Self-Adaptive and Self-Organizing Systems (SASO). (pp. 11-20). Augsburg: IEEE.

F. Silva et al.,  "Online hyper-evolution of controllers in multirobot systems", in 2016 IEEE 10th Int. Conf. on Self-Adaptive and Self-Organizing Systems (SASO), Cabri, G., Picard, G., and Suri, N., Ed., Augsburg, IEEE, 2016, pp. 11-20

@inproceedings{silva2016_1730765711579,
	author = "Silva, F. and Correia, L. and Christensen, A. L.",
	title = "Online hyper-evolution of controllers in multirobot systems",
	booktitle = "2016 IEEE 10th International Conference on Self-Adaptive and Self-Organizing Systems (SASO)",
	year = "2016",
	editor = "Cabri, G., Picard, G., and Suri, N.",
	volume = "",
	number = "",
	series = "",
	doi = "10.1109/SASO.2016.7",
	pages = "11-20",
	publisher = "IEEE",
	address = "Augsburg",
	organization = "IEEE",
	url = "https://ieeexplore.ieee.org/xpl/conhome/7774239/proceeding"
}

TY  - CPAPER
TI  - Online hyper-evolution of controllers in multirobot systems
T2  - 2016 IEEE 10th International Conference on Self-Adaptive and Self-Organizing Systems (SASO)
AU  - Silva, F.
AU  - Correia, L.
AU  - Christensen, A. L.
PY  - 2016
SP  - 11-20
DO  - 10.1109/SASO.2016.7
CY  - Augsburg
UR  - https://ieeexplore.ieee.org/xpl/conhome/7774239/proceeding
AB  - In this paper, we introduce online hyper-evolution (OHE) to accelerate and increase the performance of online evolution of robotic controllers. Robots executing OHE use the different sources of feedback information traditionally associated with controller evaluation to find effective evolutionary algorithms and controllers online during task execution. We present two approaches: OHE-fitness, which uses the fitness score of controllers as the criterion to select promising algorithms over time, and OHE-diversity, which relies on the behavioural diversity of controllers for algorithm selection. Both OHE-fitness and OHE-diversity are distributed across groups of robots that evolve in parallel. We assess the performance of OHE-fitness and of OHE-diversity in two foraging tasks with differing complexity, and in five configurations of a dynamic phototaxis task with varying evolutionary pressures. Results show that our OHE approaches: (i) outperform multiple state-of-the-art algorithms as they facilitate controllers with superior performance and faster evolution of solutions, and (ii) can increase effectiveness at different stages of evolution by combining the benefits of multiple algorithms over time. Overall, our study shows that OHE is an effective new paradigm to the synthesis of controllers for robots.
ER  -