Exportar Publicação

Gomes, J., Mariano, P. & Christensen, A. L. (2018). Dynamic team heterogeneity in cooperative coevolutionary algorithms. IEEE Transactions on Evolutionary Computation. 22 (6), 934-948

J. Gomes et al.,  "Dynamic team heterogeneity in cooperative coevolutionary algorithms", in IEEE Transactions on Evolutionary Computation, vol. 22, no. 6, pp. 934-948, 2018

@article{gomes2018_1775999450063,
	author = "Gomes, J. and Mariano, P. and Christensen, A. L.",
	title = "Dynamic team heterogeneity in cooperative coevolutionary algorithms",
	journal = "IEEE Transactions on Evolutionary Computation",
	year = "2018",
	volume = "22",
	number = "6",
	doi = "10.1109/TEVC.2017.2779840",
	pages = "934-948",
	url = "https://ieeexplore.ieee.org/document/8141987/"
}

TY  - JOUR
TI  - Dynamic team heterogeneity in cooperative coevolutionary algorithms
T2  - IEEE Transactions on Evolutionary Computation
VL  - 22
IS  - 6
AU  - Gomes, J.
AU  - Mariano, P.
AU  - Christensen, A. L.
PY  - 2018
SP  - 934-948
SN  - 1089-778X
DO  - 10.1109/TEVC.2017.2779840
UR  - https://ieeexplore.ieee.org/document/8141987/
AB  - We propose Hyb-CCEA, a cooperative coevolutionary algorithm for the evolution of genetically heterogeneous multiagent teams. The proposed approach extends the cooperative coevolution architecture with operators that put the number of coevolving populations under evolutionary control. Populations are dynamically merged based on behavioural similarity, thus decreasing team heterogeneity, and stochastic population splits are used to explore increased team heterogeneity. Hyb-CCEA is capable of converging to suitable team compositions for the given task, be it a completely homogeneous team where all agents share the same control logic, a heterogeneous team where each agent has distinct control logic, or a partially heterogeneous team. By placing both the team composition and the agent controllers under evolutionary control, Hyb-CCEA can be applied to domains for which the experimenter has limited or no knowledge about possible solutions. We study Hyb-CCEA extensively in an abstract domain, and conduct a series of validation experiments with four simulated multi-robot tasks: two multi-rover foraging tasks, and two robotic soccer tasks. The results show that Hyb-CCEA takes advantage of partial heterogeneity and frequently outperforms the standard cooperative coevolution approach, both in terms of fitness scores achieved and number of evaluations needed to evolve solutions.
ER  -