Investigating Genetic Network Programming for Multiple Nest Foraging

Fred D. Foss, Truls Stenrud, P. Haddow
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Abstract

Genetic Network Programming is a relatively unexplored evolutionary algorithm, particularly for more advanced tasks. Foraging is a challenging domain within swarm robotics, since it requires an aptitude for multiple rudimentary behaviours. The work herein thus investigates the application of Genetic Network Programming for multiple nest foraging. Further, a variant of Genetic Network Programming, which incorporates neural network benefits is proposed and evaluated. The results are compared to state-of-the-art foraging algorithms including the generic Neuro-evolution of Augmented Technologies and Novelty Search algorithms and the more application specific Multiple-Place Foraging Algorithm. Results indicate that Genetic Network Programming shows promise.
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多巢觅食的遗传网络规划研究
遗传网络规划是一种相对未开发的进化算法,特别是对于更高级的任务。在群体机器人中,觅食是一个具有挑战性的领域,因为它需要具备多种基本行为的能力。本文研究了遗传网络规划在多巢觅食中的应用。在此基础上,提出并评价了一种结合神经网络优势的遗传网络规划方法。将结果与最先进的觅食算法进行比较,包括通用的增强技术神经进化和新颖性搜索算法以及更具体的多地点觅食算法。结果表明,遗传网络规划具有良好的应用前景。
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