Quantum Genetic Algorithm for Evolving Neural Controllers
Quantum inspired genetic algorithms (QGAs) have shown their superiority against conventional genetic algorithms (CGAs) in numerous challenging applications in the recent years. This paper explores the use of QGAs to evolve neural controllers for modular robots locomotion. Modular robots
are known for their ability to operate in unknown and changing environments, which implies the need to rapidly find effective control strategies and to actively adapt to any changes in the environment. Therefore, modular robots control is considered one of the most complex tasks in robotics.
In this paper, we present a new algorithm for evolving neural controllers using real-observation QGAs. Our algorithm is used to investigate adaptive locomotion using our simulated module, which is a hybrid module with two degrees of freedoms and four connecting faces. The experiments show
that the evolved neural controllers are able to produce several stable gaits.
Keywords: Artificial Neural Networks; Locomotion; Modular Robots; Quantum Inspired Genetic Algorithm; Real Observation; Self-Reconfigurable Robots
Document Type: Research Article
Affiliations: Department of Computer Science, University of Biskra, Biskra 07000, Algeria; LESIA Laboratory, University of Biskra, Biskra 07000, Algeria
Publication date: 01 January 2018
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