{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,28]],"date-time":"2026-04-28T07:10:47Z","timestamp":1777360247797,"version":"3.51.4"},"publisher-location":"Cham","reference-count":20,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783031147203","type":"print"},{"value":"9783031147210","type":"electronic"}],"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\/978-3-031-14721-0_2","type":"book-chapter","created":{"date-parts":[[2022,8,15]],"date-time":"2022-08-15T00:02:52Z","timestamp":1660521772000},"page":"19-32","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Gene-pool Optimal Mixing in\u00a0Cartesian Genetic Programming"],"prefix":"10.1007","author":[{"given":"Joe","family":"Harrison","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4261-7511","authenticated-orcid":false,"given":"Tanja","family":"Alderliesten","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4186-6666","authenticated-orcid":false,"given":"Peter A. N.","family":"Bosman","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,8,15]]},"reference":[{"key":"2_CR1","unstructured":"Asuncion, A., Newman, D.: UCI machine learning repository (2007)"},{"key":"2_CR2","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"276","DOI":"10.1007\/978-3-642-32937-1_28","volume-title":"Parallel Problem Solving from Nature - PPSN XII","author":"PAN Bosman","year":"2012","unstructured":"Bosman, P.A.N., Thierens, D.: On measures to build linkage trees in LTGA. In: Coello, C.A.C., Cutello, V., Deb, K., Forrest, S., Nicosia, G., Pavone, M. (eds.) PPSN 2012. LNCS, vol. 7491, pp. 276\u2013285. Springer, Heidelberg (2012). https:\/\/doi.org\/10.1007\/978-3-642-32937-1_28"},{"key":"2_CR3","first-page":"1","volume":"7","author":"J Dem\u0161ar","year":"2006","unstructured":"Dem\u0161ar, J.: Statistical comparisons of classifiers over multiple data sets. J. Mach. Learn. Res. 7, 1\u201330 (2006)","journal-title":"J. Mach. Learn. Res."},{"key":"2_CR4","doi-asserted-by":"crossref","unstructured":"Dick, G., Owen, C.A., Whigham, P.A.: Feature standardisation and coefficient optimisation for effective symbolic regression. In: Proceedings of the Genetic and Evolutionary Computation Conference, pp. 306\u2013314 (2020)","DOI":"10.1145\/3377930.3390237"},{"issue":"6","key":"2_CR5","doi-asserted-by":"publisher","first-page":"205","DOI":"10.1016\/j.ipl.2007.07.002","volume":"104","author":"I Gronau","year":"2007","unstructured":"Gronau, I., Moran, S.: Optimal implementations of UPGMA and other common clustering algorithms. Inf. Process. Lett. 104(6), 205\u2013210 (2007)","journal-title":"Inf. Process. Lett."},{"key":"2_CR6","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"70","DOI":"10.1007\/3-540-36599-0_7","volume-title":"Genetic Programming","author":"M Keijzer","year":"2003","unstructured":"Keijzer, M.: Improving symbolic regression with interval arithmetic and linear scaling. In: Ryan, C., Soule, T., Keijzer, M., Tsang, E., Poli, R., Costa, E. (eds.) EuroGP 2003. LNCS, vol. 2610, pp. 70\u201382. Springer, Heidelberg (2003). https:\/\/doi.org\/10.1007\/3-540-36599-0_7"},{"key":"2_CR7","volume-title":"Genetic Programming: On the Programming of Computers by Means of Natural Selection","author":"JR Koza","year":"1992","unstructured":"Koza, J.R.: Genetic Programming: On the Programming of Computers by Means of Natural Selection, vol. 1. MIT Press, Cambridge (1992)"},{"key":"2_CR8","volume-title":"Genetic Programming II: Automatic Discovery of Reusable Programs","author":"JR Koza","year":"1994","unstructured":"Koza, J.R.: Genetic Programming II: Automatic Discovery of Reusable Programs, vol. 17. MIT Press, Cambridge (1994)"},{"issue":"3","key":"2_CR9","doi-asserted-by":"publisher","first-page":"31","DOI":"10.1145\/3236386.3241340","volume":"16","author":"ZC Lipton","year":"2018","unstructured":"Lipton, Z.C.: The mythos of model interpretability: in machine learning, the concept of interpretability is both important and slippery. Queue 16(3), 31\u201357 (2018)","journal-title":"Queue"},{"key":"2_CR10","unstructured":"Miller, J.F., et al.: 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. 2, pp. 1135\u20131142 (1999)"},{"key":"2_CR11","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s10710-019-09360-6","volume":"21","author":"JF Miller","year":"2019","unstructured":"Miller, J.F.: Cartesian genetic programming: its status and future. Genet. Program Evolvable Mach. 21, 1\u201340 (2019). https:\/\/doi.org\/10.1007\/s10710-019-09360-6","journal-title":"Genet. Program Evolvable Mach."},{"key":"2_CR12","unstructured":"Poli, R., Banzhaf, W., Langdon, W.B., Miller, J.F., Nordin, P., Fogarty, T.C.: Genetic Programming. Springer (2004)"},{"issue":"287","key":"2_CR13","doi-asserted-by":"publisher","first-page":"655","DOI":"10.1080\/01621459.1959.10501526","volume":"54","author":"JW Pratt","year":"1959","unstructured":"Pratt, J.W.: Remarks on zeros and ties in the Wilcoxon signed rank procedures. J. Am. Stat. Assoc. 54(287), 655\u2013667 (1959)","journal-title":"J. Am. Stat. Assoc."},{"issue":"2","key":"2_CR14","doi-asserted-by":"publisher","first-page":"99","DOI":"10.1162\/106365602320169811","volume":"10","author":"KO Stanley","year":"2002","unstructured":"Stanley, K.O., Miikkulainen, R.: Evolving neural networks through augmenting topologies. Evol. Comput. 10(2), 99\u2013127 (2002)","journal-title":"Evol. Comput."},{"key":"2_CR15","unstructured":"Vilone, G., Longo, L.: Explainable artificial intelligence: a systematic review. arXiv preprint arXiv:2006.00093 (2020)"},{"key":"2_CR16","doi-asserted-by":"crossref","unstructured":"Virgolin, M., Alderliesten, T., Bel, A., Witteveen, C., Bosman, P.A.: Symbolic regression and feature construction with GP-GOMEA applied to radiotherapy dose reconstruction of childhood cancer survivors. In: Proceedings of the Genetic and Evolutionary Computation Conference, pp. 1395\u20131402 (2018)","DOI":"10.1145\/3205455.3205604"},{"key":"2_CR17","doi-asserted-by":"crossref","unstructured":"Virgolin, M., Alderliesten, T., Witteveen, C., Bosman, P.A.: Scalable genetic programming by gene-pool optimal mixing and input-space entropy-based building-block learning. In: Proceedings of the Genetic and Evolutionary Computation Conference, pp. 1041\u20131048 (2017)","DOI":"10.1145\/3071178.3071287"},{"issue":"2","key":"2_CR18","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1162\/evco_a_00278","volume":"29","author":"M Virgolin","year":"2021","unstructured":"Virgolin, M., Alderliesten, T., Witteveen, C., Bosman, P.A.: Improving model-based genetic programming for symbolic regression of small expressions. Evol. Comput. 29(2), 211\u2013237 (2021)","journal-title":"Evol. Comput."},{"key":"2_CR19","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"79","DOI":"10.1007\/978-3-030-58115-2_6","volume-title":"Parallel Problem Solving from Nature \u2013 PPSN XVI","author":"M Virgolin","year":"2020","unstructured":"Virgolin, M., De Lorenzo, A., Medvet, E., Randone, F.: Learning a formula of interpretability to learn interpretable formulas. In: B\u00e4ck, T., et al. (eds.) PPSN 2020. LNCS, vol. 12270, pp. 79\u201393. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58115-2_6"},{"key":"2_CR20","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"260","DOI":"10.1007\/11729976_23","volume-title":"Genetic Programming","author":"JR Woodward","year":"2006","unstructured":"Woodward, J.R.: Complexity and cartesian genetic programming. In: Collet, P., Tomassini, M., Ebner, M., Gustafson, S., Ek\u00e1rt, A. (eds.) EuroGP 2006. LNCS, vol. 3905, pp. 260\u2013269. Springer, Heidelberg (2006). https:\/\/doi.org\/10.1007\/11729976_23"}],"container-title":["Lecture Notes in Computer Science","Parallel Problem Solving from Nature \u2013 PPSN XVII"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-14721-0_2","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,12]],"date-time":"2024-03-12T16:47:24Z","timestamp":1710262044000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-14721-0_2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031147203","9783031147210"],"references-count":20,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-14721-0_2","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"15 August 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"PPSN","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Parallel Problem Solving from Nature","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Dortmund","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Germany","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":"10 September 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14 September 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ppsn2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/ppsn2022.cs.tu-dortmund.de\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"185","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":"85","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":"0","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":"46% - 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":"3.75","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":"3.11","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":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}