{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,16]],"date-time":"2026-02-16T16:41:51Z","timestamp":1771260111923,"version":"3.50.1"},"publisher-location":"Cham","reference-count":19,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031611391","type":"print"},{"value":"9783031611407","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"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":[[2024]]},"DOI":"10.1007\/978-3-031-61140-7_10","type":"book-chapter","created":{"date-parts":[[2024,5,30]],"date-time":"2024-05-30T07:10:33Z","timestamp":1717053033000},"page":"98-107","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Enhancing Interpretability in\u00a0Machine Learning: A Focus on\u00a0Genetic Network Programming, Its Variants, and\u00a0Applications"],"prefix":"10.1007","author":[{"given":"Mohamad","family":"Roshanzamir","sequence":"first","affiliation":[]},{"given":"Roohallah","family":"Alizadehsani","sequence":"additional","affiliation":[]},{"given":"Seyed Vahid","family":"Moravvej","sequence":"additional","affiliation":[]},{"given":"Javad Hassannataj","family":"Joloudari","sequence":"additional","affiliation":[]},{"given":"Hamid","family":"Alinejad-Rokny","sequence":"additional","affiliation":[]},{"given":"Juan M.","family":"Gorriz","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,5,31]]},"reference":[{"key":"10_CR1","unstructured":"Chen, Y., Mabu, S., Hirasawa, K.: Genetic network programming with reinforcement learning and its application to creating stock trading rules. In: Machine Learning. IntechOpen (2009)"},{"key":"10_CR2","doi-asserted-by":"crossref","unstructured":"Foss, F., Stenrud, T., Haddow, P.C.: Investigating genetic network programming for multiple nest foraging. In: 2021 IEEE Symposium Series on Computational Intelligence (SSCI), pp.\u00a01\u20137. IEEE (2021)","DOI":"10.1109\/SSCI50451.2021.9659926"},{"key":"10_CR3","doi-asserted-by":"crossref","unstructured":"Gonzales, E., Shimada, K., Mabu, S., Hirasawa, K., Hu, J.: Genetic network programming with parallel processing for association rule mining in large and dense databases. In: Proceedings of the 9th Annual Conference on Genetic and Evolutionary Computation, pp. 1512\u20131512 (2007)","DOI":"10.1145\/1276958.1277241"},{"key":"10_CR4","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2023.101945","volume":"100","author":"JM G\u00f3rriz","year":"2023","unstructured":"G\u00f3rriz, J.M., et al.: Computational approaches to explainable artificial intelligence: advances in theory, applications and trends. Inf. Fus. 100, 101945 (2023)","journal-title":"Inf. Fus."},{"key":"10_CR5","doi-asserted-by":"crossref","unstructured":"Hirasawa, K., Okubo, M., Katagiri, H., Hu, J., Murata, J.: Comparison between genetic network programming (GNP) and genetic programming (GP). In: Proceedings of the 2001 Congress on Evolutionary Computation (IEEE Cat. No. 01TH8546), vol.\u00a02, pp. 1276\u20131282. IEEE (2001)","DOI":"10.1109\/CEC.2001.934337"},{"key":"10_CR6","unstructured":"Katagiri, H., Hirasawa, K., Hu, J., Murata, J.: Network structure oriented evolutionary model\u2013genetic network programming\u2013and its comparison with genetic programming. In: Proceedings of the 3rd Annual Conference on Genetic and Evolutionary Computation, pp. 179\u2013179 (2001)"},{"key":"10_CR7","doi-asserted-by":"crossref","unstructured":"Li, X., He, W., Hirasawa, K.: Adaptive genetic network programming. In: 2014 IEEE Congress on Evolutionary Computation (CEC), pp. 1808\u20131815. IEEE (2014)","DOI":"10.1109\/CEC.2014.6900290"},{"key":"10_CR8","doi-asserted-by":"crossref","unstructured":"Li, X., Hirasawa, K.: A learning classifier system based on genetic network programming. In: 2013 IEEE International Conference on Systems, Man, and Cybernetics, pp. 1323\u20131328. IEEE (2013)","DOI":"10.1109\/SMC.2013.229"},{"key":"10_CR9","doi-asserted-by":"crossref","unstructured":"Li, X., Li, B., Mabu, S., Hirasawa, K.: A continuous estimation of distribution algorithm by evolving graph structures using reinforcement learning. In: 2012 IEEE Congress on Evolutionary Computation, pp.\u00a01\u20138. IEEE (2012)","DOI":"10.1109\/CEC.2012.6256481"},{"issue":"4","key":"10_CR10","doi-asserted-by":"publisher","first-page":"339","DOI":"10.1002\/tee.21864","volume":"8","author":"X Li","year":"2013","unstructured":"Li, X., Mabu, S., Hirasawa, K.: An extended probabilistic model building genetic network programming using both of good and bad individuals. IEEJ Trans. Electr. Electron. Eng. 8(4), 339\u2013347 (2013)","journal-title":"IEEJ Trans. Electr. Electron. Eng."},{"issue":"1","key":"10_CR11","doi-asserted-by":"publisher","first-page":"98","DOI":"10.1109\/TEVC.2013.2238240","volume":"18","author":"X Li","year":"2013","unstructured":"Li, X., Mabu, S., Hirasawa, K.: A novel graph-based estimation of the distribution algorithm and its extension using reinforcement learning. IEEE Trans. Evol. Comput. 18(1), 98\u2013113 (2013)","journal-title":"IEEE Trans. Evol. Comput."},{"key":"10_CR12","doi-asserted-by":"crossref","unstructured":"Li, X., Mabu, S., Zhou, H., Shimada, K., Hirasawa, K.: Genetic network programming with estimation of distribution algorithms for class association rule mining in traffic prediction. In: IEEE Congress on Evolutionary Computation, pp.\u00a01\u20138. IEEE (2010)","DOI":"10.1109\/CEC.2010.5586456"},{"key":"10_CR13","doi-asserted-by":"publisher","first-page":"374","DOI":"10.1016\/j.eswa.2018.07.041","volume":"114","author":"X Li","year":"2018","unstructured":"Li, X., Yang, M., Wu, S.: Niching genetic network programming with rule accumulation for decision making: an evolutionary rule-based approach. Expert Syst. Appl. 114, 374\u2013387 (2018)","journal-title":"Expert Syst. Appl."},{"issue":"5","key":"10_CR14","doi-asserted-by":"publisher","first-page":"733","DOI":"10.1002\/tee.23109","volume":"15","author":"S Mabu","year":"2020","unstructured":"Mabu, S., Higuchi, T., Kuremoto, T.: Semisupervised learning for class association rule mining using genetic network programming. IEEJ Trans. Electr. Electron. Eng. 15(5), 733\u2013740 (2020)","journal-title":"IEEJ Trans. Electr. Electron. Eng."},{"key":"10_CR15","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"710","DOI":"10.1007\/978-3-540-24855-2_81","volume-title":"Genetic and Evolutionary Computation \u2013 GECCO 2004","author":"S Mabu","year":"2004","unstructured":"Mabu, S., Hirasawa, K., Hu, J.: Genetic network programming with reinforcement learning and its performance evaluation. In: Deb, K. (ed.) GECCO 2004. LNCS, vol. 3103, pp. 710\u2013711. Springer, Heidelberg (2004). https:\/\/doi.org\/10.1007\/978-3-540-24855-2_81"},{"issue":"4","key":"10_CR16","doi-asserted-by":"publisher","first-page":"117","DOI":"10.3390\/a14040117","volume":"14","author":"H Madokoro","year":"2021","unstructured":"Madokoro, H., Nix, S., Sato, K.: Automatic calibration of piezoelectric bed-leaving sensor signals using genetic network programming algorithms. Algorithms 14(4), 117 (2021)","journal-title":"Algorithms"},{"key":"10_CR17","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2019.105551","volume":"82","author":"R Ramezanian","year":"2019","unstructured":"Ramezanian, R., Peymanfar, A., Ebrahimi, S.B.: An integrated framework of genetic network programming and multi-layer perceptron neural network for prediction of daily stock return: an application in Tehran stock exchange market. Appl. Soft Comput. 82, 105551 (2019)","journal-title":"Appl. Soft Comput."},{"issue":"5","key":"10_CR18","doi-asserted-by":"publisher","first-page":"671","DOI":"10.20965\/jaciii.2022.p0671","volume":"26","author":"Y Xu","year":"2022","unstructured":"Xu, Y., Sun, Y., Ma, Z., Zhao, H., Wang, Y., Lu, N.: Attribute selection based genetic network programming for intrusion detection system. J. Adv. Comput. Intell. Intell. Inform. 26(5), 671\u2013683 (2022)","journal-title":"J. Adv. Comput. Intell. Intell. Inform."},{"key":"10_CR19","unstructured":"Zhang, Y., Li, X., Yang, Y., Mabu, S., Jin, Y., Hirasawa, K.: Functionally distributed systems using parallel genetic network programming. In: Proceedings of SICE Annual Conference 2010, pp. 2626\u20132630. IEEE (2010)"}],"container-title":["Lecture Notes in Computer Science","Artificial Intelligence for Neuroscience and Emotional Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-61140-7_10","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,14]],"date-time":"2024-10-14T17:02:40Z","timestamp":1728925360000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-61140-7_10"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031611391","9783031611407"],"references-count":19,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-61140-7_10","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"31 May 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"IWINAC","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Work-Conference on the Interplay Between Natural and Artificial Computation","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Olh\u00e2o","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Portugal","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"31 May 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"3 June 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"10","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iwinac2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/iwinac.eu\/iwinac.org\/iwinac2024\/index.html","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}