{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,27]],"date-time":"2026-04-27T18:59:33Z","timestamp":1777316373055,"version":"3.51.4"},"publisher-location":"Cham","reference-count":53,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032230041","type":"print"},{"value":"9783032230058","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026]]},"DOI":"10.1007\/978-3-032-23005-8_3","type":"book-chapter","created":{"date-parts":[[2026,4,27]],"date-time":"2026-04-27T18:30:53Z","timestamp":1777314653000},"page":"34-50","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Comparative Study on\u00a0Robustness in\u00a0Evolved Image Classifiers"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-2354-5702","authenticated-orcid":false,"given":"Camilo","family":"De La Torre","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-9461-9952","authenticated-orcid":false,"given":"St\u00e9phane","family":"Treillard","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3214-0142","authenticated-orcid":false,"given":"Camille","family":"Franchet","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8675-197X","authenticated-orcid":false,"given":"Herv\u00e9","family":"Luga","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2414-0051","authenticated-orcid":false,"given":"Dennis","family":"Wilson","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1360-1932","authenticated-orcid":false,"given":"Sylvain","family":"Cussat-Blanc","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,4,28]]},"reference":[{"key":"3_CR1","unstructured":"Adam, K.D.B.J., et\u00a0al.: A method for stochastic optimization. arXiv preprint arXiv:1412.69801412(6) (2014)"},{"key":"3_CR2","doi-asserted-by":"publisher","unstructured":"Alter, F., Matsushita, Y., Tang, X.: An intensity similarity measure in low-light conditions. In: Leonardis, A., Bischof, H., Pinz, A. (eds) ECCV 2006. LNCS, vol. 3954, pp. 267\u2013280. Springer, Heidelberg (2006). https:\/\/doi.org\/10.1007\/11744085_21","DOI":"10.1007\/11744085_21"},{"key":"3_CR3","doi-asserted-by":"publisher","unstructured":"Ansel, J., et al.: PyTorch 2: faster machine learning through dynamic python bytecode transformation and graph compilation. In: 29th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, vol. 2 (ASPLOS 2024). ACM (2024). https:\/\/doi.org\/10.1145\/3620665.3640366. https:\/\/docs.pytorch.org\/assets\/pytorch2-2.pdf","DOI":"10.1145\/3620665.3640366"},{"key":"3_CR4","doi-asserted-by":"publisher","unstructured":"Bejnordi, B.E., et al.: Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer. J. Am. Med. Assoc. (JAMA) (2017). https:\/\/doi.org\/10.1001\/JAMA.2017.14585","DOI":"10.1001\/JAMA.2017.14585"},{"key":"3_CR5","doi-asserted-by":"crossref","unstructured":"B\u2019ethune, L., Boissin, T., Serrurier, M., Mamalet, F., Friedrich, C., Gonz\u00e1lez-Sanz, A.: Pay attention to your loss: understanding misconceptions about lipschitz neural networks. Neural Inf. Process. Syst. (2021)","DOI":"10.52202\/068431-1460"},{"key":"3_CR6","doi-asserted-by":"publisher","DOI":"10.1109\/TEVC.2020.3002229","author":"Y Bi","year":"2021","unstructured":"Bi, Y., Xue, B., Zhang, M.: Genetic programming with image-related operators and a flexible program structure for feature learning in image classification. IEEE Trans. Evol. Comput. (2021). https:\/\/doi.org\/10.1109\/TEVC.2020.3002229","journal-title":"IEEE Trans. Evol. Comput."},{"key":"3_CR7","doi-asserted-by":"publisher","DOI":"10.1038\/S41467-023-42664-X","author":"K Cortacero","year":"2023","unstructured":"Cortacero, K., et al.: Evolutionary design of explainable algorithms for biomedical image segmentation. Nat. Commun. (2023). https:\/\/doi.org\/10.1038\/S41467-023-42664-X","journal-title":"Nat. Commun."},{"key":"3_CR8","doi-asserted-by":"publisher","first-page":"173","DOI":"10.1007\/978-3-031-89991-1_11","volume-title":"Genetic Programming","author":"C De La Torre","year":"2025","unstructured":"De La Torre, C., et al.: Evolved and transparent pipelines for biomedical image classification. In: Xue, B., Manzoni, L., Bakurov, I. (eds.) Genetic Programming, pp. 173\u2013189. Springer, Cham (2025)"},{"key":"3_CR9","doi-asserted-by":"publisher","DOI":"10.1038\/NATURE21056","author":"A Esteva","year":"2017","unstructured":"Esteva, A., et al.: Dermatologist-level classification of skin cancer with deep neural networks. Nature (2017). https:\/\/doi.org\/10.1038\/NATURE21056","journal-title":"Nature"},{"key":"3_CR10","doi-asserted-by":"publisher","DOI":"10.1109\/MCI.2024.3446148","author":"Q Fan","year":"2024","unstructured":"Fan, Q., Bi, Y., Xue, B., Zhang, M.: A multi-tree genetic programming-based ensemble approach to image classification with limited training data [research frontier]. IEEE Comput. Intell. Mag. (2024). https:\/\/doi.org\/10.1109\/MCI.2024.3446148","journal-title":"IEEE Comput. Intell. Mag."},{"key":"3_CR11","doi-asserted-by":"publisher","unstructured":"Geirhos, R., Temme, C.R.M., Rauber, J., Sch\u00fctt, H.H., Bethge, M., Wichmann, F.A.: Generalisation in humans and deep neural networks. Neural Inf. Process. Syst. (2018). https:\/\/doi.org\/10.15496\/PUBLIKATION-30814","DOI":"10.15496\/PUBLIKATION-30814"},{"issue":"6","key":"3_CR12","doi-asserted-by":"publisher","first-page":"e406","DOI":"10.1016\/S2589-7500(22)00063-2","volume":"4","author":"JW Gichoya","year":"2022","unstructured":"Gichoya, J.W., et al.: AI recognition of patient race in medical imaging: a modelling study. Lancet Digit. Health 4(6), e406\u2013e414 (2022)","journal-title":"Lancet Digit. Health"},{"key":"3_CR13","unstructured":"Goodfellow, I., Shlens, J., Szegedy, C.: Explaining and harnessing adversarial examples. In: International Conference on Learning Representations (2015)"},{"key":"3_CR14","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4614-6846-2_3","author":"S Harding","year":"2013","unstructured":"Harding, S., Leitner, J., Schmidhuber, J.: Cartesian genetic programming for image processing. Sci. Eng. Faculty (2013). https:\/\/doi.org\/10.1007\/978-1-4614-6846-2_3","journal-title":"Sci. Eng. Faculty"},{"key":"3_CR15","doi-asserted-by":"publisher","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Delving deep into rectifiers: surpassing human-level performance on imagenet classification. In: International Conference on Computer Vision (2015). https:\/\/doi.org\/10.1109\/ICCV.2015.123","DOI":"10.1109\/ICCV.2015.123"},{"key":"3_CR16","doi-asserted-by":"publisher","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770\u2013778 (2016). https:\/\/doi.org\/10.1109\/CVPR.2016.90","DOI":"10.1109\/CVPR.2016.90"},{"key":"3_CR17","unstructured":"Hendrycks, D., Dietterich, T.G.: Benchmarking neural network robustness to common corruptions and surface variations. arXiv:\u00a0Learning (2018)"},{"key":"3_CR18","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2024.3518575","author":"P Jena","year":"2024","unstructured":"Jena, P., Mishra, D., Das, K., Mishra, S.: NoiseAugmentNet-HHO: enhancing histopathological image classification through noise augmentation. IEEE Access (2024). https:\/\/doi.org\/10.1109\/ACCESS.2024.3518575","journal-title":"IEEE Access"},{"key":"3_CR19","doi-asserted-by":"publisher","unstructured":"Khan, A., Qureshi, A.S., Wahab, N., Hussain, M., Hamza, M.Y.: A recent survey on the applications of genetic programming in image processing. In: International Conference on Climate Informatics (2021). https:\/\/doi.org\/10.1111\/COIN.12459","DOI":"10.1111\/COIN.12459"},{"key":"3_CR20","doi-asserted-by":"publisher","DOI":"10.1145\/3065386","author":"A Krizhevsky","year":"2012","unstructured":"Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. Commun. ACM (2012). https:\/\/doi.org\/10.1145\/3065386","journal-title":"Commun. ACM"},{"key":"3_CR21","doi-asserted-by":"crossref","unstructured":"Leitner, J., Harding, S., F\u00f6rster, A., Schmidhuber, J.: Mars terrain image classification using cartesian genetic programming. In: International Symposium on Artificial Intelligence (2012)","DOI":"10.1007\/978-1-4614-6846-2_3"},{"key":"3_CR22","doi-asserted-by":"publisher","unstructured":"Litjens, G., et al.: A survey on deep learning in medical image analysis. Med. Image Anal. (2017). https:\/\/doi.org\/10.1016\/J.MEDIA.2017.07.005","DOI":"10.1016\/J.MEDIA.2017.07.005"},{"key":"3_CR23","doi-asserted-by":"publisher","DOI":"10.1145\/3546872","author":"H Liu","year":"2021","unstructured":"Liu, H., et al.: Trustworthy AI: a computational perspective. ACM Trans. Intell. Syst. Technol. (2021). https:\/\/doi.org\/10.1145\/3546872","journal-title":"ACM Trans. Intell. Syst. Technol."},{"key":"3_CR24","doi-asserted-by":"publisher","DOI":"10.1109\/TEVC.2022.3225509","author":"Y Mei","year":"2023","unstructured":"Mei, Y., Chen, Q., Lensen, A., Xue, B., Zhang, M.: Explainable artificial intelligence by genetic programming: a survey. IEEE Trans. Evol. Comput. (2023). https:\/\/doi.org\/10.1109\/TEVC.2022.3225509","journal-title":"IEEE Trans. Evol. Comput."},{"key":"3_CR25","unstructured":"Miller, J.F.: An empirical study of the efficiency of learning Boolean functions using a cartesian genetic programming approach. In: Proceedings of the Genetic and Evolutionary Computation Conference, vol.\u00a02, pp. 1135\u20131142 (1999)"},{"key":"3_CR26","doi-asserted-by":"publisher","DOI":"10.1007\/S10710-019-09360-6","author":"JF Miller","year":"2020","unstructured":"Miller, J.F.: Cartesian genetic programming: its status and future. Genet. Program Evolvable Mach. (2020). https:\/\/doi.org\/10.1007\/S10710-019-09360-6","journal-title":"Genet. Program Evolvable Mach."},{"key":"3_CR27","unstructured":"Miyato, T., Kataoka, T., Koyama, M., Yoshida, Y.: Spectral normalization for generative adversarial networks. In: International Conference on Learning Representations (2018)"},{"key":"3_CR28","doi-asserted-by":"publisher","unstructured":"Nadizar, G., Medvet, E., Wilson, D.G.: Naturally interpretable control policies via graph-based genetic programming. In: European Conference on Genetic Programming (2024). https:\/\/doi.org\/10.1007\/978-3-031-56957-9_5","DOI":"10.1007\/978-3-031-56957-9_5"},{"key":"3_CR29","doi-asserted-by":"publisher","DOI":"10.1007\/S11517-006-0077-6","author":"RJ Nandi","year":"2006","unstructured":"Nandi, R.J., Nandi, A.K., Rangayyan, R.M., Scutt, D.: Classification of breast masses in mammograms using genetic programming and feature selection. Med. Biol. Eng. Comput. (2006). https:\/\/doi.org\/10.1007\/S11517-006-0077-6","journal-title":"Med. Biol. Eng. Comput."},{"key":"3_CR30","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2025.111225","volume":"159","author":"MHM Noor","year":"2025","unstructured":"Noor, M.H.M., Ige, A.O.: A survey on state-of-the-art deep learning applications and challenges. Eng. Appl. Artif. Intell. 159, 111225 (2025)","journal-title":"Eng. Appl. Artif. Intell."},{"key":"3_CR31","unstructured":"Paszke, A., et al.: PyTorch: an imperative style, high-performance deep learning library. arXiv:\u00a0Learning (2019)"},{"key":"3_CR32","doi-asserted-by":"publisher","unstructured":"Poli, R.: Genetic programming for image analysis. Cognitive Science Research Papers-University of Birmingham CSRP (1996). https:\/\/doi.org\/10.7551\/MITPRESS\/3242.003.0053","DOI":"10.7551\/MITPRESS\/3242.003.0053"},{"key":"3_CR33","unstructured":"Recht, B.: Do imagenet classifiers generalize to imagenet? arXiv:\u00a0Computer Vision and Pattern Recognition (2019)"},{"key":"3_CR34","doi-asserted-by":"publisher","unstructured":"Serrurier, M., Mamalet, F., Gonz\u00e1lez-Sanz, A., Boissin, T., Loubes, J., Barrio, E.d.: Achieving robustness in classification using optimal transport with hinge regularization. In: Computer Vision and Pattern Recognition (2021). https:\/\/doi.org\/10.1109\/CVPR46437.2021.00057","DOI":"10.1109\/CVPR46437.2021.00057"},{"key":"3_CR35","doi-asserted-by":"publisher","DOI":"10.1186\/S40537-019-0197-0","author":"C Shorten","year":"2019","unstructured":"Shorten, C., Khoshgoftaar, T.M.: A survey on image data augmentation for deep learning. J. Big Data (2019). https:\/\/doi.org\/10.1186\/S40537-019-0197-0","journal-title":"J. Big Data"},{"key":"3_CR36","doi-asserted-by":"publisher","DOI":"10.5555\/2627435.2670313","author":"N Srivastava","year":"2014","unstructured":"Srivastava, N., Hinton, G.E., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. (2014). https:\/\/doi.org\/10.5555\/2627435.2670313","journal-title":"J. Mach. Learn. Res."},{"key":"3_CR37","unstructured":"Stacke, K., Eilertsen, G., Unger, J., Lundstr\u00f6m, C.: A closer look at domain shift for deep learning in histopathology. arXiv:\u00a0Computer Vision and Pattern Recognition (2019)"},{"key":"3_CR38","unstructured":"Sugiyama, M., Sato, I., Tsuzuku, Y.: Lipschitz-margin training: scalable certification of perturbation invariance for deep neural networks. In: NeurIPS (2018)"},{"key":"3_CR39","unstructured":"Szegedy, C., et al.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2014)"},{"key":"3_CR40","unstructured":"Teh, E.W., Taylor, G.W.: Metric learning for patch classification in digital pathology (2019)"},{"key":"3_CR41","doi-asserted-by":"publisher","unstructured":"Teh, E.W., Taylor, G.W.: Learning with less data via weakly labeled patch classification in digital pathology. In: IEEE International Symposium on Biomedical Imaging (2020). https:\/\/doi.org\/10.1109\/ISBI45749.2020.9098533","DOI":"10.1109\/ISBI45749.2020.9098533"},{"key":"3_CR42","doi-asserted-by":"publisher","unstructured":"Tellez, D., et al.: Quantifying the effects of data augmentation and stain color normalization in convolutional neural networks for computational pathology. Med. Image Anal. (2019). https:\/\/doi.org\/10.1016\/J.MEDIA.2019.101544","DOI":"10.1016\/J.MEDIA.2019.101544"},{"key":"3_CR43","doi-asserted-by":"publisher","unstructured":"Torre, C.D.L., Cortacero, K., Cussat-Blanc, S., Wilson, D.G.: Multimodal adaptive graph evolution. In: Proceedings of the Genetic and Evolutionary Computation Conference Companion (2024). https:\/\/doi.org\/10.1145\/3638530.3654347","DOI":"10.1145\/3638530.3654347"},{"key":"3_CR44","doi-asserted-by":"publisher","unstructured":"Torre, C.D.L., Lavinas, Y., Cortacero, K., Luga, H., Wilson, D.G., Cussat-Blanc, S.: Multimodal adaptive graph evolution for program synthesis. In: Affenzeller, M., et al. (eds.) PPSN 2024. LNCS, vol. 15148, pp. 306\u2013321. Springer, Cham (2024). https:\/\/doi.org\/10.1007\/978-3-031-70055-2_19","DOI":"10.1007\/978-3-031-70055-2_19"},{"key":"3_CR45","doi-asserted-by":"publisher","unstructured":"Torre, C.D.L., Nadizar, G., Lavinas, Y., Luga, H., Wilson, D.G., Cussat-Blanc, S.: Evolution of inherently interpretable visual control policies. In: Proceedings of the Genetic and Evolutionary Computation Conference (2025). https:\/\/doi.org\/10.1145\/3712256.3726332","DOI":"10.1145\/3712256.3726332"},{"key":"3_CR46","unstructured":"Tsipras, D., Santurkar, S., Engstrom, L., Turner, A., M\u0105dry, A.: Robustness may be at odds with accuracy. In: International Conference on Learning Representations (2018)"},{"key":"3_CR47","doi-asserted-by":"publisher","unstructured":"Veeling, B.S., Linmans, J., Winkens, J., Cohen, T., Welling, M.: Rotation equivariant CNNs for digital pathology. In: Frangi, A., Schnabel, J., Davatzikos, C., Alberola-L\u00f3pez, C., Fichtinger, G. (eds) MICCAI 2018. LNCS, vol. 11071, pp. 210\u2013218. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-00934-2_24","DOI":"10.1007\/978-3-030-00934-2_24"},{"key":"3_CR48","doi-asserted-by":"publisher","DOI":"10.1083\/JCB.200903097","author":"J Waters","year":"2009","unstructured":"Waters, J.: Accuracy and precision in quantitative fluorescence microscopy. J. Cell Biol. (2009). https:\/\/doi.org\/10.1083\/JCB.200903097","journal-title":"J. Cell Biol."},{"key":"3_CR49","doi-asserted-by":"publisher","unstructured":"Wu, Z., Xue, B., Zhang, M.: Multitree genetic programming for multimodal learning in multimodal medical image classification. IEEE Trans. Evol. Comput. PP, 1 (2025). https:\/\/doi.org\/10.1109\/TEVC.2025.3619532","DOI":"10.1109\/TEVC.2025.3619532"},{"key":"3_CR50","doi-asserted-by":"publisher","DOI":"10.1016\/J.ESWA.2025.128487","author":"W Xue","year":"2025","unstructured":"Xue, W.: Deep learning framework for enhanced MRI analysis in healthcare diagnosis. Expert Syst. Appl. (2025). https:\/\/doi.org\/10.1016\/J.ESWA.2025.128487","journal-title":"Expert Syst. Appl."},{"key":"3_CR51","unstructured":"Yoshida, Y., Miyato, T.: Spectral norm regularization for improving the generalizability of deep learning. arXiv:\u00a0Machine Learning (2017)"},{"key":"3_CR52","doi-asserted-by":"publisher","DOI":"10.1371\/JOURNAL.PMED.1002683","author":"J Zech","year":"2018","unstructured":"Zech, J.: Variable generalization performance of a deep learning model to detect pneumonia in chest radiographs: a cross-sectional study. PLoS Med. (2018). https:\/\/doi.org\/10.1371\/JOURNAL.PMED.1002683","journal-title":"PLoS Med."},{"key":"3_CR53","unstructured":"Zhang, C., Bengio, S., Hardt, M., Recht, B., Vinyals, O.: Understanding deep learning requires rethinking generalization. In: International Conference on Learning Representations (2016)"}],"container-title":["Lecture Notes in Computer Science","Genetic Programming"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-23005-8_3","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,27]],"date-time":"2026-04-27T18:31:03Z","timestamp":1777314663000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-23005-8_3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"ISBN":["9783032230041","9783032230058"],"references-count":53,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-23005-8_3","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026]]},"assertion":[{"value":"28 April 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"The authors have no competing interests to declare that are relevant to the content of this article.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Disclosure of Interests"}},{"value":"EuroGP","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"European Conference on Genetic Programming (Part of EvoStar)","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Toulouse","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"France","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2026","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 April 2026","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"10 April 2026","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"eurogp2026","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.evostar.org\/2026\/eurogp\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}