An investigation of environmental influence on the benefits of adaptation mechanisms in evolutionary swarm robotics

Steyven, Andreas; Hart, Emma; Paechter, Ben

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, July). An investigation of environmental influence on the benefits of adaptation mechanisms in evolutionary swarm robotics. Presented at Genetic and Evolutionary Computation Conference - GECCO '17

• Presentation Conference Type: Conference Paper (published)
• 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: May 3, 2017
• 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
• Contract Date: May 3, 2017