학술논문

Diversity Improves Teamwork: Optimising Teams using a Genetic Algorithm
Document Type
Conference
Source
2019 IEEE Congress on Evolutionary Computation (CEC) Evolutionary Computation (CEC), 2019 IEEE Congress on. :2848-2855 Jun, 2019
Subject
Communication, Networking and Broadcast Technologies
Computing and Processing
General Topics for Engineers
Robotics and Control Systems
Genetic algorithms
Task analysis
Teamwork
Optimization
Sensors
Computational modeling
agent-based modelling
genetic algorithm
collaboration
teamwork
personality psychology
Language
Abstract
Collaboration is fundamental to our society, but how should we best build teamsƒ 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.