Cited By
View all- Shorten DNitschke G(2015)Evolving Generalised Maze SolversApplications of Evolutionary Computation10.1007/978-3-319-16549-3_63(783-794)Online publication date: 17-Mar-2015
Generational neuro-evolution: restart and retry for improvement
Pages 225 - 232
Abstract
This paper proposes a new Neuro-Evolution (NE) method for automated controller design in agent-based systems. The method is Generational Neuro-Evolution (GeNE), and is comparatively evaluated with established NE methods in a multi-agent predator-prey task. This study is part of an ongoing research goal to derive efficient (minimising convergence time to optimal solutions) and scalable (effective for increasing numbers of agents) controller design methods for adapting agents in neuro-evolutionary multi-agent systems. Dissimilar to comparative NE methods, GeNE employs tiered selection and evaluation as its generational fitness evaluation mechanism and, furthermore, re-initializes the population each generation. Results indicate that GeNE is an appropriate controller design method for achieving efficient and scalable behavior in a multi-agent predator-prey task, where the goal was for multiple predator agents to collectively capture a prey agent. GeNE outperforms comparative NE methods in terms of efficiency (minimising the number of genotype evaluations to attain optimal task performance).
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Index Terms
- Generational neuro-evolution: restart and retry for improvement
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Published: 12 July 2014
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View all- Shorten DNitschke G(2015)Evolving Generalised Maze SolversApplications of Evolutionary Computation10.1007/978-3-319-16549-3_63(783-794)Online publication date: 17-Mar-2015
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