{"title":"Investigating Genetic Network Programming for Multiple Nest Foraging","authors":"Fred D. Foss, Truls Stenrud, P. Haddow","doi":"10.1109/SSCI50451.2021.9659926","DOIUrl":null,"url":null,"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.","PeriodicalId":255763,"journal":{"name":"2021 IEEE Symposium Series on Computational Intelligence (SSCI)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 IEEE Symposium Series on Computational Intelligence (SSCI)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SSCI50451.2021.9659926","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
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.