{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,14]],"date-time":"2026-01-14T15:02:53Z","timestamp":1768402973517,"version":"3.49.0"},"publisher-location":"Cham","reference-count":45,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030299323","type":"print"},{"value":"9783030299330","type":"electronic"}],"license":[{"start":{"date-parts":[[2019,8,30]],"date-time":"2019-08-30T00:00:00Z","timestamp":1567123200000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020]]},"DOI":"10.1007\/978-3-030-29933-0_16","type":"book-chapter","created":{"date-parts":[[2019,8,29]],"date-time":"2019-08-29T06:32:58Z","timestamp":1567060378000},"page":"189-200","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Hyper-parameter Optimisation by Restrained Stochastic Hill Climbing"],"prefix":"10.1007","author":[{"given":"Rhys","family":"Stubbs","sequence":"first","affiliation":[]},{"given":"Kevin","family":"Wilson","sequence":"additional","affiliation":[]},{"given":"Shahin","family":"Rostami","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2019,8,30]]},"reference":[{"issue":"1","key":"16_CR1","doi-asserted-by":"publisher","first-page":"54","DOI":"10.1109\/72.265960","volume":"5","author":"PJ Angeline","year":"1994","unstructured":"Angeline, P.J., Saunders, G.M., Pollack, J.B.: An evolutionary algorithm that constructs recurrent neural networks. IEEE Trans. Neural Netw. 5(1), 54\u201365 (1994)","journal-title":"IEEE Trans. Neural Netw."},{"key":"16_CR2","volume-title":"Adaptive Control Processes: A Guided Tour","author":"RE Bellman","year":"2015","unstructured":"Bellman, R.E.: Adaptive Control Processes: A Guided Tour, vol. 2045. Princeton University Press, Princeton (2015)"},{"key":"16_CR3","first-page":"281","volume":"13","author":"J Bergstra","year":"2012","unstructured":"Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. J. Mach. Learn. Res. 13, 281\u2013305 (2012)","journal-title":"J. Mach. Learn. Res."},{"key":"16_CR4","unstructured":"Bergstra, J.S., Bardenet, R., Bengio, Y., K\u00e9gl, B.: Algorithms for hyper-parameter optimization. In: Advances in Neural Information Processing Systems, pp. 2546\u20132554 (2011)"},{"key":"16_CR5","unstructured":"Conti, E., Madhavan, V., Such, F.P., Lehman, J., Stanley, K., Clune, J.: Improving exploration in evolution strategies for deep reinforcement learning via a population of novelty-seeking agents. In: Advances in Neural Information Processing Systems, pp. 5027\u20135038 (2018)"},{"key":"16_CR6","doi-asserted-by":"crossref","unstructured":"Dernoncourt, F., Lee, J.Y.: Optimizing neural network hyperparameters with Gaussian processes for dialog act classification. In: 2016 IEEE Spoken Language Technology Workshop (SLT), pp. 406\u2013413. IEEE (2016)","DOI":"10.1109\/SLT.2016.7846296"},{"issue":"4\/5","key":"16_CR7","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1147\/JRD.2017.2709578","volume":"61","author":"GI Diaz","year":"2017","unstructured":"Diaz, G.I., Fokoue-Nkoutche, A., Nannicini, G., Samulowitz, H.: An effective algorithm for hyperparameter optimization of neural networks. IBM J. Res. Dev. 61(4\/5), 1\u20139 (2017)","journal-title":"IBM J. Res. Dev."},{"key":"16_CR8","unstructured":"Eggensperger, K., Feurer, M., Hutter, F., Bergstra, J., Snoek, J., Hoos, H., Leyton-Brown, K.: Towards an empirical foundation for assessing Bayesian optimization of hyperparameters. In: NIPS Workshop on Bayesian Optimization in Theory and Practice, vol.\u00a010, p.\u00a03 (2013)"},{"key":"16_CR9","doi-asserted-by":"crossref","unstructured":"Eggensperger, K., Hutter, F., Hoos, H., Leyton-Brown, K.: Efficient benchmarking of hyperparameter optimizers via surrogates. In: Twenty-Ninth AAAI Conference on Artificial Intelligence (2015)","DOI":"10.1609\/aaai.v29i1.9375"},{"key":"16_CR10","doi-asserted-by":"crossref","unstructured":"Feurer, M., Springenberg, J.T., Hutter, F.: Initializing Bayesian hyperparameter optimization via meta-learning. In: Twenty-Ninth AAAI Conference on Artificial Intelligence (2015)","DOI":"10.1609\/aaai.v29i1.9354"},{"key":"16_CR11","doi-asserted-by":"publisher","DOI":"10.1201\/9781420050646.ptb2","volume-title":"Neural Network Topologies","author":"E Fiesler","year":"1996","unstructured":"Fiesler, E.: Neural Network Topologies. Springer, Boston (1996)"},{"key":"16_CR12","unstructured":"Yufeng, G.: The 7 steps of machine learning (2017). https:\/\/towardsdatascience.com\/the-7-steps-of-machine-learning-2877d7e5548e"},{"key":"16_CR13","doi-asserted-by":"crossref","unstructured":"Gomez, F., Schmidhuber, J., Miikkulainen, R.: Efficient non-linear control through neuroevolution. In: European Conference on Machine Learning, pp. 654\u2013662. Springer (2006)","DOI":"10.1007\/11871842_64"},{"issue":"May","key":"16_CR14","first-page":"937","volume":"9","author":"F Gomez","year":"2008","unstructured":"Gomez, F., Schmidhuber, J., Miikkulainen, R.: Accelerated neural evolution through cooperatively coevolved synapses. J. Mach. Learn. Res. 9(May), 937\u2013965 (2008)","journal-title":"J. Mach. Learn. Res."},{"key":"16_CR15","unstructured":"Gomez, F.J.: Robust non-linear control through neuroevolution. Ph.D. thesis (2003)"},{"key":"16_CR16","doi-asserted-by":"crossref","unstructured":"Hagg, A., Mensing, M., Asteroth, A.: Evolving parsimonious networks by mixing activation functions. In: Proceedings of the Genetic and Evolutionary Computation Conference, pp. 425\u2013432. ACM (2017)","DOI":"10.1145\/3071178.3071275"},{"issue":"4","key":"16_CR17","doi-asserted-by":"publisher","first-page":"152","DOI":"10.1016\/j.jalgor.2009.04.002","volume":"64","author":"V Heidrich-Meisner","year":"2009","unstructured":"Heidrich-Meisner, V., Igel, C.: Neuroevolution strategies for episodic reinforcement learning. J. Algorithms 64(4), 152\u2013168 (2009)","journal-title":"J. Algorithms"},{"key":"16_CR18","doi-asserted-by":"crossref","unstructured":"Ilievski, I., Akhtar, T., Feng, J., Shoemaker, C.A.: Efficient hyperparameter optimization for deep learning algorithms using deterministic RBF surrogates. In: Thirty-First AAAI Conference on Artificial Intelligence (2017)","DOI":"10.1609\/aaai.v31i1.10647"},{"key":"16_CR19","doi-asserted-by":"crossref","unstructured":"Larochelle, H., Erhan, D., Courville, A., Bergstra, J., Bengio, Y.: An empirical evaluation of deep architectures on problems with many factors of variation. In: Proceedings of the 24th International Conference on Machine Learning, pp. 473\u2013480. ACM (2007)","DOI":"10.1145\/1273496.1273556"},{"key":"16_CR20","unstructured":"Miconi, T., Clune, J., Stanley, K.O.: Differentiable plasticity: training plastic neural networks with backpropagation. arXiv preprint arXiv:1804.02464 (2018)"},{"key":"16_CR21","first-page":"762","volume":"89","author":"DJ Montana","year":"1989","unstructured":"Montana, D.J., Davis, L.: Training feedforward neural networks using genetic algorithms. Int. Jt. Conf. Artif. Intell. 89, 762\u2013767 (1989)","journal-title":"Int. Jt. Conf. Artif. Intell."},{"key":"16_CR22","doi-asserted-by":"crossref","unstructured":"Morse, G., Stanley, K.O.: Simple evolutionary optimization can rival stochastic gradient descent in neural networks. In: Proceedings of the Genetic and Evolutionary Computation Conference 2016, pp. 477\u2013484. ACM (2016)","DOI":"10.1145\/2908812.2908916"},{"key":"16_CR23","unstructured":"Rechenber, I.: Optimierung technischer systeme nach prinzipien der biologischen evolution. Ph.D. thesis, Verlag nicht ermittelbar (1970)"},{"key":"16_CR24","unstructured":"Risi, S., Togelius, J.: Neuroevolution in games: state of the art and open challenges. CoRR abs\/1410.7326 (2014). http:\/\/arxiv.org\/abs\/1410.7326"},{"key":"16_CR25","unstructured":"Rostami, S.: Preference focussed many-objective evolutionary computation. Ph.D. thesis, Manchester Metropolitan University (2014)"},{"issue":"4","key":"16_CR26","doi-asserted-by":"publisher","first-page":"313","DOI":"10.3233\/ICA-160529","volume":"23","author":"S Rostami","year":"2016","unstructured":"Rostami, S., Neri, F.: Covariance matrix adaptation pareto archived evolution strategy with hypervolume-sorted adaptive grid algorithm. Integr. Comput.-Aided Eng. 23(4), 313\u2013329 (2016)","journal-title":"Integr. Comput.-Aided Eng."},{"key":"16_CR27","doi-asserted-by":"publisher","first-page":"494","DOI":"10.1016\/j.ins.2014.10.031","volume":"295","author":"S Rostami","year":"2015","unstructured":"Rostami, S., O\u2019Reilly, D., Shenfield, A., Bowring, N.: A novel preference articulation operator for the evolutionary multi-objective optimisation of classifiers in concealed weapons detection. Inf. Sci. 295, 494\u2013520 (2015)","journal-title":"Inf. Sci."},{"key":"16_CR28","volume-title":"Artificial intelligence: a modern approach","author":"SJ Russell","year":"2016","unstructured":"Russell, S.J., Norvig, P.: Artificial intelligence: a modern approach. Pearson Education Limited, Malaysia (2016)"},{"key":"16_CR29","unstructured":"Salimans, T., Ho, J., Chen, X., Sidor, S., Sutskever, I.: Evolution strategies as a scalable alternative to reinforcement learning. preprint arXiv:1703.03864 (2017)"},{"key":"16_CR30","doi-asserted-by":"crossref","unstructured":"Siebel, N.T., Botel, J., Sommer, G.: Efficient neural network pruning during neuro-evolution. In: 2009 International Joint Conference on Neural Networks, pp. 2920\u20132927. IEEE (2009)","DOI":"10.1109\/IJCNN.2009.5179035"},{"key":"16_CR31","unstructured":"Snoek, J., Larochelle, H., Adams, R.P.: Practical Bayesian optimization of machine learning algorithms. In: Advances in neural information processing systems, pp. 2951\u20132959 (2012)"},{"issue":"2","key":"16_CR32","doi-asserted-by":"publisher","first-page":"185","DOI":"10.1162\/artl.2009.15.2.15202","volume":"15","author":"KO Stanley","year":"2009","unstructured":"Stanley, K.O., D\u2019Ambrosio, D.B., Gauci, J.: A hypercube-based encoding for evolving large-scale neural networks. Artif. life 15(2), 185\u2013212 (2009)","journal-title":"Artif. life"},{"issue":"2","key":"16_CR33","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":"16_CR34","unstructured":"Storn, R.: On the usage of differential evolution for function optimization. In: Proceedings of North American Fuzzy Information Processing, pp. 519\u2013523. IEEE (1996)"},{"key":"16_CR35","unstructured":"Such, F.P., Madhavan, V., Conti, E., Lehman, J., Stanley, K.O., Clune, J.: Deep neuroevolution: genetic algorithms are a competitive alternative for training deep neural networks for reinforcement learning. arXiv preprint arXiv:1712.06567 (2017)"},{"issue":"3","key":"16_CR36","first-page":"30","volume":"23","author":"J Togelius","year":"2009","unstructured":"Togelius, J., Schaul, T., Wierstra, D., Igel, C., Gomez, F., Schmidhuber, J.: Ontogenetic and phylogenetic reinforcement learning. K\u00fcnstliche Intell. 23(3), 30\u201333 (2009)","journal-title":"K\u00fcnstliche Intell."},{"key":"16_CR37","unstructured":"Wagenaartje, T.: wagenaartje\/neataptic (2018). https:\/\/github.com\/wagenaartje\/neataptic"},{"key":"16_CR38","doi-asserted-by":"crossref","unstructured":"Wang, L., Feng, M., Zhou, B., Xiang, B., Mahadevan, S.: Efficient hyper-parameter optimization for NLP applications. In: Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, pp. 2112\u20132117 (2015)","DOI":"10.18653\/v1\/D15-1253"},{"issue":"5","key":"16_CR39","doi-asserted-by":"publisher","first-page":"197","DOI":"10.1177\/003754977101700504","volume":"17","author":"R White Jr","year":"1971","unstructured":"White Jr., R.: A survey of random methods for parameter optimization. Simulation 17(5), 197\u2013205 (1971)","journal-title":"Simulation"},{"issue":"3","key":"16_CR40","doi-asserted-by":"publisher","first-page":"347","DOI":"10.1016\/0167-8191(90)90086-O","volume":"14","author":"D Whitley","year":"1990","unstructured":"Whitley, D., Starkweather, T., Bogart, C.: Genetic algorithms and neural networks: optimizing connections and connectivity. Parallel Comput. 14(3), 347\u2013361 (1990)","journal-title":"Parallel Comput."},{"issue":"6","key":"16_CR41","doi-asserted-by":"publisher","first-page":"80","DOI":"10.2307\/3001968","volume":"1","author":"F Wilcoxon","year":"1945","unstructured":"Wilcoxon, F.: Individual comparisons by ranking methods. Biom. Bull. 1(6), 80\u201383 (1945)","journal-title":"Biom. Bull."},{"issue":"9","key":"16_CR42","doi-asserted-by":"publisher","first-page":"1423","DOI":"10.1109\/5.784219","volume":"87","author":"X Yao","year":"1999","unstructured":"Yao, X.: Evolving artificial neural networks. Proc. IEEE 87(9), 1423\u20131447 (1999)","journal-title":"Proc. IEEE"},{"issue":"3","key":"16_CR43","doi-asserted-by":"publisher","first-page":"694","DOI":"10.1109\/72.572107","volume":"8","author":"X Yao","year":"1997","unstructured":"Yao, X., Liu, Y.: A new evolutionary system for evolving artificial neural networks. Trans. Neur. Netw. 8(3), 694\u2013713 (1997)","journal-title":"Trans. Neur. Netw."},{"issue":"3","key":"16_CR44","doi-asserted-by":"publisher","first-page":"694","DOI":"10.1109\/72.572107","volume":"8","author":"X Yao","year":"1997","unstructured":"Yao, X., Liu, Y.: A new evolutionary system for evolving artificial neural networks. IEEE Trans. Neural Networks 8(3), 694\u2013713 (1997)","journal-title":"IEEE Trans. Neural Networks"},{"key":"16_CR45","doi-asserted-by":"crossref","unstructured":"Young, S.R., Rose, D.C., Karnowski, T.P., Lim, S.H., Patton, R.M.: Optimizing deep learning hyper-parameters through an evolutionary algorithm. In: Proceedings of the Workshop on Machine Learning in High-Performance Computing Environments, p.\u00a04. ACM (2015)","DOI":"10.1145\/2834892.2834896"}],"container-title":["Advances in Intelligent Systems and Computing","Advances in Computational Intelligence Systems"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-29933-0_16","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,9,26]],"date-time":"2022-09-26T18:13:20Z","timestamp":1664216000000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-29933-0_16"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,8,30]]},"ISBN":["9783030299323","9783030299330"],"references-count":45,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-29933-0_16","relation":{},"ISSN":["2194-5357","2194-5365"],"issn-type":[{"value":"2194-5357","type":"print"},{"value":"2194-5365","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,8,30]]},"assertion":[{"value":"30 August 2019","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"UKCI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"UK Workshop on Computational Intelligence","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Portsmouth","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"United Kingdom","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2019","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"11 September 2019","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"13 September 2019","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ukci2019","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}