Genetic-Algorithm Seeding Of Idiotypic Networks For Mobile-Robot Navigation
Amanda Whitbrook, Uwe Aickelin, Jonathan Garibaldi

TL;DR
This paper introduces a genetic algorithm-based method for evolving diverse, adaptable behaviour sets in robot navigation systems using idiotypic networks, significantly enhancing flexibility and diversity in robot responses.
Contribution
It presents a novel encoding of robot behaviours combined with a genetic algorithm to develop multiple diverse behaviour sets for adaptive navigation.
Findings
Rapid development of successful behaviour sets within 25 minutes
Greater diversity achieved with multiple autonomous populations
Effective navigation around maze using evolved behaviours
Abstract
Robot-control designers have begun to exploit the properties of the human immune system in order to produce dynamic systems that can adapt to complex, varying, real-world tasks. Jernes idiotypic-network theory has proved the most popular artificial-immune-system (AIS) method for incorporation into behaviour-based robotics, since idiotypic selection produces highly adaptive responses. However, previous efforts have mostly focused on evolving the network connections and have often worked with a single, pre-engineered set of behaviours, limiting variability. This paper describes a method for encoding behaviours as a variable set of attributes, and shows that when the encoding is used with a genetic algorithm (GA), multiple sets of diverse behaviours can develop naturally and rapidly, providing much greater scope for flexible behaviour-selection. The algorithm is tested extensively with a…
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