Dr Andreas Steyven A.Steyven@napier.ac.uk
Lecturer
An investigation of environmental influence on the benefits of adaptation mechanisms in evolutionary swarm robotics
Steyven, Andreas; Hart, Emma; Paechter, Ben
Authors
Prof Emma Hart E.Hart@napier.ac.uk
Professor
Prof Ben Paechter B.Paechter@napier.ac.uk
Professor
Abstract
A robotic swarm that is required to operate for long periods in a potentially unknown environment can use both evolution and individual learning methods in order to adapt. However, the role played by the environment in influencing the effectiveness of each type of learning is not well understood. In this paper, we specifically address this question by analysing the performance of a swarm in a range of simulated, dynamic environments where a distributed evolutionary algorithm for evolving a controller is augmented with a number of different individual learning mechanisms. The learning mechanisms themselves are defined by parameters which can be either fixed or inherited. We conduct experiments in a range of dynamic environments whose characteristics are varied so as to present different opportunities for learning. Results enable us to map environmental characteristics to the most effective learning algorithm.
Citation
Steyven, A., Hart, E., & Paechter, B. (2017). An investigation of environmental influence on the benefits of adaptation mechanisms in evolutionary swarm robotics. In GECCO '17 Proceedings of the Genetic and Evolutionary Computation Conference (155-162). https://doi.org/10.1145/3071178.3071232
Conference Name | Genetic and Evolutionary Computation Conference - GECCO '17 |
---|---|
Start Date | Jul 15, 2017 |
End Date | Jul 19, 2017 |
Acceptance Date | Mar 20, 2017 |
Online Publication Date | Jan 1, 2017 |
Publication Date | 2017 |
Deposit Date | Apr 3, 2017 |
Publicly Available Date | Mar 28, 2024 |
Publisher | Association for Computing Machinery (ACM) |
Pages | 155-162 |
Book Title | GECCO '17 Proceedings of the Genetic and Evolutionary Computation Conference |
Chapter Number | unknown |
ISBN | 9781450349208 |
DOI | https://doi.org/10.1145/3071178.3071232 |
Keywords | Evolutionary Swarm Robotics, Environment, Learning |
Public URL | http://researchrepository.napier.ac.uk/Output/823533 |
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© 2017 ACM. This is the author's version of the work. It is posted
here for your personal use. Not for redistribution. The denitive
Version of Record was published in Proceedings of GECCO '17, July
15-19, 2017, http://dx.doi.org/http://dx.doi.org/10.1145/3071178.
3071232.
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Copyright Statement
© 2017 ACM. This is the author's version of the work. It is posted
here for your personal use. Not for redistribution. The denitive
Version of Record was published in Proceedings of GECCO '17, July
15-19, 2017, http://dx.doi.org/http://dx.doi.org/10.1145/3071178.
3071232.
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