Lim, S;
Bentley, P;
(2019)
Diversity Improves Teamwork: Optimising Teams using a Genetic Algorithm.
In:
Proceedings of the 2019 IEEE Congress on Evolutionary Computation (CEC).
(pp. pp. 2878-2855).
IEEE: Wellington, New Zealand, New Zealand.
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Abstract
Collaboration is fundamental to our society, but how should we best build teams? We investigate by applying optimisation to an agent-based model of collaboration. The model takes inspiration from particle swarm optimisation, abstracting a shared goal as a shared optimisation task, and modelling the personality differences in team members as strategies for moving within, interpreting and sharing information about the solution space. We update the model and add a genetic algorithm in order to investigate the effects of differing initial ideas within teams of different personality combinations as they attempt to collaborate to achieve their shared task. We run experiments on homogeneous teams with similar personalities and heterogeneous teams with random personalities and find that increased diversity in team members' initial ideas significantly improves teamwork, and more so for teams comprising individuals with similar personalities.
Type: | Proceedings paper |
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Title: | Diversity Improves Teamwork: Optimising Teams using a Genetic Algorithm |
Event: | 2019 IEEE Congress on Evolutionary Computation (CEC) |
Open access status: | An open access version is available from UCL Discovery |
DOI: | 10.1109/CEC.2019.8789981 |
Publisher version: | https://doi.org/10.1109/CEC.2019.8789981 |
Language: | English |
Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
Keywords: | agent-based modelling, genetic algorithm, collaboration, teamwork, personality psychology |
UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
URI: | https://discovery.ucl.ac.uk/id/eprint/10078224 |




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