{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,29]],"date-time":"2025-09-29T11:41:22Z","timestamp":1759146082460,"version":"3.40.3"},"publisher-location":"Cham","reference-count":24,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783031201752"},{"type":"electronic","value":"9783031201769"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-031-20176-9_12","type":"book-chapter","created":{"date-parts":[[2022,10,28]],"date-time":"2022-10-28T20:03:45Z","timestamp":1666987425000},"page":"142-154","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Extracting Symbolic Models of\u00a0Collective Behaviors with\u00a0Graph Neural Networks and\u00a0Macro-Micro Evolution"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8918-8163","authenticated-orcid":false,"given":"Stephen","family":"Powers","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7695-0027","authenticated-orcid":false,"given":"Joshua","family":"Smith","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2155-0445","authenticated-orcid":false,"given":"Carlo","family":"Pinciroli","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,10,29]]},"reference":[{"key":"12_CR1","series-title":"Natural Computing Series","doi-asserted-by":"publisher","first-page":"73","DOI":"10.1007\/978-3-030-72069-8_5","volume-title":"Automated Design of Machine Learning and Search Algorithms","author":"M Birattari","year":"2021","unstructured":"Birattari, M., Ligot, A., Francesca, G.: AutoMoDe: a modular approach to the automatic off-line design and fine-tuning of control software for robot swarms. In: Pillay, N., Qu, R. (eds.) Automated Design of Machine Learning and Search Algorithms. NCS, pp. 73\u201390. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-72069-8_5"},{"key":"12_CR2","series-title":"Springer Proceedings in Advanced Robotics","doi-asserted-by":"publisher","first-page":"447","DOI":"10.1007\/978-3-319-73008-0_31","volume-title":"Distributed Autonomous Robotic Systems","author":"DS Brown","year":"2018","unstructured":"Brown, D.S., Turner, R., Hennigh, O., Loscalzo, S.: Discovery and exploration of novel swarm behaviors given limited robot capabilities. In: Gro\u00df, R., et al. (eds.) Distributed Autonomous Robotic Systems. SPAR, vol. 6, pp. 447\u2013460. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-319-73008-0_31"},{"key":"12_CR3","unstructured":"Camazine, S., Deneubourg, J.L., Franks, N.R., Sneyd, J., Theraulaz, G., Bonabeau, E.: Self-Organization in Biological Systems. Princeton Studies in Complexity. Princeton (2003)"},{"issue":"5","key":"12_CR4","doi-asserted-by":"publisher","first-page":"792","DOI":"10.1109\/tevc.2017.2683489","volume":"21","author":"Q Chen","year":"2017","unstructured":"Chen, Q., Zhang, M., Xue, B.: Feature selection to improve generalization of genetic programming for high-dimensional symbolic regression. IEEE Trans. Evol. Comput. 21(5), 792\u2013806 (2017). https:\/\/doi.org\/10.1109\/tevc.2017.2683489","journal-title":"IEEE Trans. Evol. Comput."},{"key":"12_CR5","doi-asserted-by":"publisher","unstructured":"Cranmer, M.: PySR: fast & parallelized symbolic regression in Python\/Julia (2020). https:\/\/doi.org\/10.5281\/zenodo.4041459","DOI":"10.5281\/zenodo.4041459"},{"key":"12_CR6","unstructured":"Cranmer, M.D., et al.: Discovering symbolic models from deep learning with inductive biases. CoRR abs\/2006.11287 (2020). https:\/\/arxiv.org\/abs\/2006.11287"},{"key":"12_CR7","doi-asserted-by":"publisher","unstructured":"Ferrante, E., Du\u00e9\u00f1ez-Guzm\u00e1n, E., Turgut, A.E., Wenseleers, T.: GESwarm: grammatical evolution for the automatic synthesis of collective behaviors in swarm robotics. In: Proceedings of the 15th Annual Conference on Genetic and Evolutionary Computation, GECCO 2013, pp. 17\u201324. Association for Computing Machinery, New York (2013). https:\/\/doi.org\/10.1145\/2463372.2463385","DOI":"10.1145\/2463372.2463385"},{"issue":"2","key":"12_CR8","doi-asserted-by":"publisher","first-page":"125","DOI":"10.1007\/s11721-015-0107-9","volume":"9","author":"G Francesca","year":"2015","unstructured":"Francesca, G., et al.: AutoMoDe-chocolate: automatic design of control software for robot swarms. Swarm Intell. 9(2), 125\u2013152 (2015). https:\/\/doi.org\/10.1007\/s11721-015-0107-9","journal-title":"Swarm Intell."},{"issue":"10","key":"12_CR9","doi-asserted-by":"publisher","first-page":"7523","DOI":"10.1007\/s00500-019-04379-4","volume":"24","author":"Z Huang","year":"2019","unstructured":"Huang, Z., Zhong, J., Feng, L., Mei, Y., Cai, W.: A fast parallel genetic programming framework with adaptively weighted primitives for symbolic regression. Soft. Comput. 24(10), 7523\u20137539 (2019). https:\/\/doi.org\/10.1007\/s00500-019-04379-4","journal-title":"Soft. Comput."},{"key":"12_CR10","doi-asserted-by":"publisher","unstructured":"Kaufmann, R., Gupta, P., Taylor, J.: An active inference model of collective intelligence. Entropy 23(7) (2021). https:\/\/doi.org\/10.3390\/e23070830, https:\/\/www.mdpi.com\/1099-4300\/23\/7\/830","DOI":"10.3390\/e23070830"},{"key":"12_CR11","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"30","DOI":"10.1007\/978-3-030-00533-7_3","volume-title":"Swarm Intelligence","author":"J Kuckling","year":"2018","unstructured":"Kuckling, J., Ligot, A., Bozhinoski, D., Birattari, M.: Behavior trees as a control architecture in the automatic modular design of robot swarms. In: Dorigo, M., Birattari, M., Blum, C., Christensen, A.L., Reina, A., Trianni, V. (eds.) ANTS 2018. LNCS, vol. 11172, pp. 30\u201343. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-00533-7_3"},{"key":"12_CR12","doi-asserted-by":"publisher","unstructured":"La Cava, W., Spector, L., Danai, K.: Epsilon-lexicase selection for regression. In: Proceedings of the Genetic and Evolutionary Computation Conference 2016 (2016). https:\/\/doi.org\/10.1145\/2908812.2908898","DOI":"10.1145\/2908812.2908898"},{"key":"12_CR13","unstructured":"Li, Q., Gama, F., Ribeiro, A., Prorok, A.: Graph neural networks for decentralized multi-robot path planning. CoRR abs\/1912.06095 (2019). http:\/\/arxiv.org\/abs\/1912.06095"},{"key":"12_CR14","doi-asserted-by":"publisher","unstructured":"Motta, F.A., Freitas, J.M.D., Souza, F.R.D., Bernardino, H.S., Oliveira, I.L.D., Barbosa, H.J.: A hybrid grammar-based genetic programming for symbolic regression problems. In: 2018 IEEE Congress on Evolutionary Computation (CEC) (2018). https:\/\/doi.org\/10.1109\/cec.2018.8477826","DOI":"10.1109\/cec.2018.8477826"},{"key":"12_CR15","doi-asserted-by":"publisher","unstructured":"Neupane, A., Goodrich, M.: Learning swarm behaviors using grammatical evolution and behavior trees. In: Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, pp. 513\u2013520. International Joint Conferences on Artificial Intelligence Organization, Macao (2019). https:\/\/doi.org\/10.24963\/ijcai.2019\/73","DOI":"10.24963\/ijcai.2019\/73"},{"key":"12_CR16","doi-asserted-by":"publisher","unstructured":"Orzechowski, P., La Cava, W., Moore, J.H.: Where are we now? In: Proceedings of the Genetic and Evolutionary Computation Conference (2018). https:\/\/doi.org\/10.1145\/3205455.3205539, http:\/\/dx.doi.org\/10.1145\/3205455.3205539","DOI":"10.1145\/3205455.3205539"},{"key":"12_CR17","doi-asserted-by":"publisher","unstructured":"Reynolds, C.W.: Flocks, herds and schools: a distributed behavioral model. In: SIGGRAPH 1987: Proceedings of the 14th Annual Conference on Computer Graphics and Interactive Techniques, pp. 25\u201334 (1987). https:\/\/doi.org\/10.1145\/37401.37406","DOI":"10.1145\/37401.37406"},{"issue":"2","key":"12_CR18","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1371\/journal.pone.0212044","volume":"14","author":"K Ried","year":"2019","unstructured":"Ried, K., M\u00fcller, T., Briegel, H.J.: Modelling collective motion based on the principle of agency: General framework and the case of marching locusts. PLOS One 14(2), 1\u201321 (2019). https:\/\/doi.org\/10.1371\/journal.pone.0212044","journal-title":"PLOS One"},{"issue":"11","key":"12_CR19","doi-asserted-by":"publisher","first-page":"8639","DOI":"10.1063\/1.462271","volume":"96","author":"B Smit","year":"1992","unstructured":"Smit, B.: Phase diagrams of Lennard-Jones fluids. J. Chem. Phys. 96(11), 8639\u20138640 (1992). https:\/\/doi.org\/10.1063\/1.462271","journal-title":"J. Chem. Phys."},{"key":"12_CR20","unstructured":"Tolstaya, E., Gama, F., Paulos, J., Pappas, G., Kumar, V., Ribeiro, A.: Learning decentralized controllers for robot swarms with graph neural networks. In: Kaelbling, L.P., Kragic, D., Sugiura, K. (eds.) Proceedings of the Conference on Robot Learning. Proceedings of Machine Learning Research, 30 October\u201301 November 2020, vol. 100, pp. 671\u2013682. PMLR (2020). https:\/\/proceedings.mlr.press\/v100\/tolstaya20a.html"},{"key":"12_CR21","doi-asserted-by":"publisher","unstructured":"Udrescu, S.M., Tegmark, M.: AI Feynman: a physics-inspired method for symbolic regression. Sci. Adv. 6(16), eaay2631 (2020). https:\/\/doi.org\/10.1126\/sciadv.aay2631, https:\/\/www.science.org\/doi\/abs\/10.1126\/sciadv.aay2631","DOI":"10.1126\/sciadv.aay2631"},{"issue":"2","key":"12_CR22","doi-asserted-by":"publisher","first-page":"191","DOI":"10.1162\/106454601753139005","volume":"7","author":"CR Ward","year":"2001","unstructured":"Ward, C.R., Gobet, F., Kendall, G.: Evolving collective behavior in an artificial ecology. Artif. Life 7(2), 191\u2013209 (2001). https:\/\/doi.org\/10.1162\/106454601753139005","journal-title":"Artif. Life"},{"key":"12_CR23","doi-asserted-by":"publisher","unstructured":"White, T., Salehi-Abari, A.: A swarm-based crossover operator for genetic programming. Proceedings of the 10th Annual Conference on Genetic and Evolutionary Computation - GECCO 2008 (2008). https:\/\/doi.org\/10.1145\/1389095.1389356","DOI":"10.1145\/1389095.1389356"},{"key":"12_CR24","doi-asserted-by":"publisher","first-page":"4492","DOI":"10.1109\/TSMC.2018.2853719","volume":"50","author":"J Zhong","year":"2020","unstructured":"Zhong, J., Feng, L., Cai, W., Ong, Y.: Multifactorial genetic programming for symbolic regression problems. IEEE Trans. Syst. Man Cybern. Syst. 50, 4492\u20134505 (2020)","journal-title":"IEEE Trans. Syst. Man Cybern. Syst."}],"container-title":["Lecture Notes in Computer Science","Swarm Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-20176-9_12","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,9,26]],"date-time":"2023-09-26T20:32:43Z","timestamp":1695760363000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-20176-9_12"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031201752","9783031201769"],"references-count":24,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-20176-9_12","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"29 October 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ANTS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Swarm Intelligence","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Malaga","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Spain","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2 November 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4 November 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"13","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"antsw2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/ants2022.uma.es\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Easy chair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"45","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"19","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"14","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"42% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3,0222","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2,3076","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"4 extended abstracts","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}