{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T04:10:49Z","timestamp":1750219849594,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":66,"publisher":"ACM","license":[{"start":{"date-parts":[[2023,7,12]],"date-time":"2023-07-12T00:00:00Z","timestamp":1689120000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"Danish Council for Independent Research","award":["913100042B"],"award-info":[{"award-number":["913100042B"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2023,7,15]]},"DOI":"10.1145\/3583131.3590460","type":"proceedings-article","created":{"date-parts":[[2024,2,18]],"date-time":"2024-02-18T06:51:19Z","timestamp":1708239079000},"page":"1248-1256","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Learning to Act through Evolution of Neural Diversity in Random Neural Networks"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7884-9432","authenticated-orcid":false,"given":"Joachim","family":"Pedersen","sequence":"first","affiliation":[{"name":"Digital Design, IT University of Copenhagen, Copenhagen, Denmark"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3607-8400","authenticated-orcid":false,"given":"Sebastian","family":"Risi","sequence":"additional","affiliation":[{"name":"Digital Design, IT University of Copenhagen, Copenhagen, Denmark"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2023,7,12]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"Synaptic plasticity: taming the beast. Nature neuroscience 3, 11","author":"Abbott Larry F","year":"2000","unstructured":"Larry F Abbott and Sacha B Nelson. 2000. Synaptic plasticity: taming the beast. Nature neuroscience 3, 11 (2000), 1178--1183."},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1109\/TSMC.1983.6313077"},{"key":"e_1_3_2_1_3_1","volume-title":"The quest for the golden activation function. arXiv preprint arXiv:1808.00783","author":"Basirat Mina","year":"2018","unstructured":"Mina Basirat and Peter M Roth. 2018. The quest for the golden activation function. arXiv preprint arXiv:1808.00783 (2018)."},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cell.2018.08.045"},{"key":"e_1_3_2_1_5_1","volume-title":"Representation learning: A review and new perspectives","author":"Bengio Yoshua","year":"2013","unstructured":"Yoshua Bengio, Aaron Courville, and Pascal Vincent. 2013. Representation learning: A review and new perspectives. IEEE transactions on pattern analysis and machine intelligence 35, 8 (2013), 1798--1828."},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuron.2021.07.002"},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1109\/72.623216"},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1145\/3377930.3389841"},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2022.01.001"},{"key":"e_1_3_2_1_10_1","volume-title":"Openai gym. arXiv preprint arXiv:1606.01540","author":"Brockman Greg","year":"2016","unstructured":"Greg Brockman, Vicki Cheung, Ludwig Pettersson, Jonas Schneider, John Schulman, Jie Tang, and Wojciech Zaremba. 2016. Openai gym. arXiv preprint arXiv:1606.01540 (2016)."},{"volume-title":"Connectionist Models","author":"Chalmers David J","key":"e_1_3_2_1_11_1","unstructured":"David J Chalmers. 1991. The evolution of learning: An experiment in genetic connectionism. In Connectionist Models. Elsevier, 81--90."},{"key":"e_1_3_2_1_12_1","volume-title":"Meta-Reinforcement Learning with Self-Modifying Networks. In 36th Conference on Neural Information Processing Systems (NeurIPS","author":"Chalvidal Mathieu","year":"2022","unstructured":"Mathieu Chalvidal, Thomas Serre, and Rufin Van-Rullen. 2022. Meta-Reinforcement Learning with Self-Modifying Networks. In 36th Conference on Neural Information Processing Systems (NeurIPS 2022). 1--19."},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1109\/GUCON.2018.8675097"},{"key":"e_1_3_2_1_14_1","volume-title":"Dzmitry Bahdanau, and Yoshua Bengio.","author":"Cho Kyunghyun","year":"2014","unstructured":"Kyunghyun Cho, Bart Van Merri\u00ebnboer, Dzmitry Bahdanau, and Yoshua Bengio. 2014. On the properties of neural machine translation: Encoder-decoder approaches. arXiv preprint arXiv:1409.1259 (2014)."},{"key":"e_1_3_2_1_15_1","volume-title":"Finding structure in time. Cognitive science 14, 2","author":"Elman Jeffrey L","year":"1990","unstructured":"Jeffrey L Elman. 1990. Finding structure in time. Cognitive science 14, 2 (1990), 179--211."},{"key":"e_1_3_2_1_16_1","volume-title":"An introduction to bio-inspired artificial neural network architectures. Acta neurologica belgica 103, 1","author":"Fasel B","year":"2003","unstructured":"B Fasel. 2003. An introduction to bio-inspired artificial neural network architectures. Acta neurologica belgica 103, 1 (2003), 6--12."},{"volume-title":"Bio-inspired artificial intelligence: theories, methods, and technologies","author":"Floreano Dario","key":"e_1_3_2_1_17_1","unstructured":"Dario Floreano and Claudio Mattiussi. 2008. Bio-inspired artificial intelligence: theories, methods, and technologies. MIT press."},{"key":"e_1_3_2_1_18_1","volume-title":"The lottery ticket hypothesis: Finding sparse, trainable neural networks. arXiv preprint arXiv:1803.03635","author":"Frankle Jonathan","year":"2018","unstructured":"Jonathan Frankle and Michael Carbin. 2018. The lottery ticket hypothesis: Finding sparse, trainable neural networks. arXiv preprint arXiv:1803.03635 (2018)."},{"key":"e_1_3_2_1_19_1","volume-title":"Daniel M Roy, and Michael Carbin.","author":"Frankle Jonathan","year":"2019","unstructured":"Jonathan Frankle, Gintare Karolina Dziugaite, Daniel M Roy, and Michael Carbin. 2019. Stabilizing the lottery ticket hypothesis. arXiv preprint arXiv:1903.01611 (2019)."},{"key":"e_1_3_2_1_20_1","volume-title":"Weight agnostic neural networks. Advances in neural information processing systems 32","author":"Gaier Adam","year":"2019","unstructured":"Adam Gaier and David Ha. 2019. Weight agnostic neural networks. Advances in neural information processing systems 32 (2019)."},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1109\/DSD53832.2021.00083"},{"key":"e_1_3_2_1_22_1","volume-title":"Associative memory in a network ofbiological'neurons. Advances in neural information processing systems 3","author":"Gerstner Wulfram","year":"1990","unstructured":"Wulfram Gerstner. 1990. Associative memory in a network ofbiological'neurons. Advances in neural information processing systems 3 (1990)."},{"key":"e_1_3_2_1_23_1","volume-title":"Evolving Stable Strategies. blog.otoro.net","author":"David Ha.","year":"2017","unstructured":"David Ha. 2017. Evolving Stable Strategies. blog.otoro.net (2017). http:\/\/blog.otoro.net\/2017\/11\/12\/evolving-stable-strategies\/"},{"key":"e_1_3_2_1_24_1","volume-title":"A visual guide to evolution strategies. blog. otoro. net","author":"David Ha.","year":"2017","unstructured":"David Ha. 2017. A visual guide to evolution strategies. blog. otoro. net (2017)."},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1145\/3071178.3071275"},{"key":"e_1_3_2_1_26_1","volume-title":"The CMA evolution strategy: a comparing review. Towards a new evolutionary computation","author":"Hansen Nikolaus","year":"2006","unstructured":"Nikolaus Hansen. 2006. The CMA evolution strategy: a comparing review. Towards a new evolutionary computation (2006), 75--102."},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuron.2017.06.011"},{"key":"e_1_3_2_1_28_1","volume-title":"A powerful generative model using random weights for the deep image representation. Advances in Neural Information Processing Systems 29","author":"He Kun","year":"2016","unstructured":"Kun He, Yan Wang, and John Hopcroft. 2016. A powerful generative model using random weights for the deep image representation. Advances in Neural Information Processing Systems 29 (2016)."},{"key":"e_1_3_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.123"},{"key":"e_1_3_2_1_30_1","volume-title":"Long short-term memory. Neural computation 9, 8","author":"Hochreiter Sepp","year":"1997","unstructured":"Sepp Hochreiter and J\u00fcrgen Schmidhuber. 1997. Long short-term memory. Neural computation 9, 8 (1997), 1735--1780."},{"key":"e_1_3_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNN.2003.820440"},{"key":"e_1_3_2_1_32_1","volume-title":"Polychronization: computation with spikes. Neural computation 18, 2","author":"Izhikevich Eugene M","year":"2006","unstructured":"Eugene M Izhikevich. 2006. Polychronization: computation with spikes. Neural computation 18, 2 (2006), 245--282."},{"volume-title":"Dynamical systems in neuroscience","author":"Izhikevich Eugene M","key":"e_1_3_2_1_33_1","unstructured":"Eugene M Izhikevich. 2007. Dynamical systems in neuroscience. MIT press."},{"volume-title":"Advances in psychology.","author":"Jordan Michael I","key":"e_1_3_2_1_34_1","unstructured":"Michael I Jordan. 1997. Serial order: A parallel distributed processing approach. In Advances in psychology. Vol. 121. Elsevier, 471--495."},{"key":"e_1_3_2_1_35_1","volume-title":"Deep learning. nature 521, 7553","author":"LeCun Yann","year":"2015","unstructured":"Yann LeCun, Yoshua Bengio, and Geoffrey Hinton. 2015. Deep learning. nature 521, 7553 (2015), 436--444."},{"key":"e_1_3_2_1_36_1","volume-title":"Pruning filters for efficient convnets. arXiv preprint arXiv:1608.08710","author":"Li Hao","year":"2016","unstructured":"Hao Li, Asim Kadav, Igor Durdanovic, Hanan Samet, and Hans Peter Graf. 2016. Pruning filters for efficient convnets. arXiv preprint arXiv:1608.08710 (2016)."},{"key":"e_1_3_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0960-9822(97)70080-0"},{"key":"e_1_3_2_1_38_1","first-page":"13539","article-title":"Evolving normalization-activation layers","volume":"33","author":"Liu Hanxiao","year":"2020","unstructured":"Hanxiao Liu, Andy Brock, Karen Simonyan, and Quoc Le. 2020. Evolving normalization-activation layers. Advances in Neural Information Processing Systems 33 (2020), 13539--13550.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_39_1","volume-title":"International Conference on Machine Learning. PMLR, 6682--6691","author":"Malach Eran","year":"2020","unstructured":"Eran Malach, Gilad Yehudai, Shai Shalev-Schwartz, and Ohad Shamir. 2020. Proving the lottery ticket hypothesis: Pruning is all you need. In International Conference on Machine Learning. PMLR, 6682--6691."},{"key":"e_1_3_2_1_40_1","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.93.24.13481"},{"key":"e_1_3_2_1_41_1","volume-title":"Learning to learn with backpropagation of Hebbian plasticity. arXiv preprint arXiv:1609.02228","author":"Miconi Thomas","year":"2016","unstructured":"Thomas Miconi. 2016. Learning to learn with backpropagation of Hebbian plasticity. arXiv preprint arXiv:1609.02228 (2016)."},{"volume-title":"Growing adaptive machines","author":"Mouret Jean-Baptiste","key":"e_1_3_2_1_42_1","unstructured":"Jean-Baptiste Mouret and Paul Tonelli. 2014. Artificial evolution of plastic neural networks: a few key concepts. In Growing adaptive machines. Springer, 251--261."},{"key":"e_1_3_2_1_43_1","volume-title":"Meta-learning through hebbian plasticity in random networks. Advances in Neural Information Processing Systems 33","author":"Najarro Elias","year":"2020","unstructured":"Elias Najarro and Sebastian Risi. 2020. Meta-learning through hebbian plasticity in random networks. Advances in Neural Information Processing Systems 33 (2020)."},{"key":"e_1_3_2_1_44_1","volume-title":"Activation functions: Comparison of trends in practice and research for deep learning. arXiv","author":"Nwankpa Chigozie","year":"2018","unstructured":"Chigozie Nwankpa, Winifred Ijomah, Anthony Gachagan, and Stephen Marshall. 2018. Activation functions: Comparison of trends in practice and research for deep learning. arXiv 2018. arXiv preprint arXiv:1811.03378 (2018)."},{"key":"e_1_3_2_1_45_1","doi-asserted-by":"publisher","DOI":"10.1162\/evco_a_00282"},{"key":"e_1_3_2_1_46_1","volume-title":"Evolving and merging hebbian learning rules: increasing generalization by decreasing the number of rules. arXiv preprint arXiv:2104.07959","author":"Pedersen Joachim Winther","year":"2021","unstructured":"Joachim Winther Pedersen and Sebastian Risi. 2021. Evolving and merging hebbian learning rules: increasing generalization by decreasing the number of rules. arXiv preprint arXiv:2104.07959 (2021)."},{"key":"e_1_3_2_1_47_1","volume-title":"Deep learning with spiking neurons: opportunities and challenges. Frontiers in neuroscience","author":"Pfeiffer Michael","year":"2018","unstructured":"Michael Pfeiffer and Thomas Pfeil. 2018. Deep learning with spiking neurons: opportunities and challenges. Frontiers in neuroscience (2018), 774."},{"key":"e_1_3_2_1_48_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01191"},{"key":"e_1_3_2_1_49_1","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN.2012.6252826"},{"key":"e_1_3_2_1_50_1","volume-title":"Evolution strategies as a scalable alternative to reinforcement learning. arXiv preprint arXiv:1703.03864","author":"Salimans Tim","year":"2017","unstructured":"Tim Salimans, Jonathan Ho, Xi Chen, Szymon Sidor, and Ilya Sutskever. 2017. Evolution strategies as a scalable alternative to reinforcement learning. arXiv preprint arXiv:1703.03864 (2017)."},{"key":"e_1_3_2_1_51_1","volume-title":"Deep learning in neural networks: An overview. Neural networks 61","author":"Schmidhuber J\u00fcrgen","year":"2015","unstructured":"J\u00fcrgen Schmidhuber. 2015. Deep learning in neural networks: An overview. Neural networks 61 (2015), 85--117."},{"key":"e_1_3_2_1_52_1","doi-asserted-by":"publisher","DOI":"10.1523\/JNEUROSCI.22-06-02083.2002"},{"key":"e_1_3_2_1_53_1","unstructured":"Ivan Soltesz et al. 2006. Diversity in the neuronal machine: order and variability in interneuronal microcircuits. Oxford University Press."},{"key":"e_1_3_2_1_54_1","volume-title":"Proceedings of the 11th international conference on artificial life (Alife XI). MIT Press, 569--576","author":"Soltoggio Andrea","year":"2008","unstructured":"Andrea Soltoggio, John A Bullinaria, Claudio Mattiussi, Peter D\u00fcrr, and Dario Floreano. 2008. Evolutionary advantages of neuromodulated plasticity in dynamic, reward-based scenarios. In Proceedings of the 11th international conference on artificial life (Alife XI). MIT Press, 569--576."},{"key":"e_1_3_2_1_55_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2018.07.013"},{"key":"e_1_3_2_1_56_1","volume-title":"Evolving neural networks through augmenting topologies. Evolutionary computation 10, 2","author":"Stanley Kenneth O","year":"2002","unstructured":"Kenneth O Stanley and Risto Miikkulainen. 2002. Evolving neural networks through augmenting topologies. Evolutionary computation 10, 2 (2002), 99--127."},{"key":"e_1_3_2_1_57_1","volume-title":"Neural plasticity and cognitive development. Developmental neuropsychology 18, 2","author":"Stiles Joan","year":"2000","unstructured":"Joan Stiles. 2000. Neural plasticity and cognitive development. Developmental neuropsychology 18, 2 (2000), 237--272."},{"key":"e_1_3_2_1_58_1","volume-title":"Timoth\u00e9e Masquelier, and Anthony Maida.","author":"Tavanaei Amirhossein","year":"2019","unstructured":"Amirhossein Tavanaei, Masoud Ghodrati, Saeed Reza Kheradpisheh, Timoth\u00e9e Masquelier, and Anthony Maida. 2019. Deep learning in spiking neural networks. Neural networks 111 (2019), 47--63."},{"key":"e_1_3_2_1_59_1","doi-asserted-by":"publisher","DOI":"10.1145\/3530811"},{"key":"e_1_3_2_1_60_1","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0079138"},{"key":"e_1_3_2_1_61_1","volume-title":"Proceedings of the IEEE conference on computer vision and pattern recognition. 9446--9454","author":"Ulyanov Dmitry","year":"2018","unstructured":"Dmitry Ulyanov, Andrea Vedaldi, and Victor Lempitsky. 2018. Deep image prior. In Proceedings of the IEEE conference on computer vision and pattern recognition. 9446--9454."},{"key":"e_1_3_2_1_62_1","volume-title":"Evolution of adaptive synapses: Robots with fast adaptive behavior in new environments. Evolutionary computation 9, 4","author":"Urzelai Joseba","year":"2001","unstructured":"Joseba Urzelai and Dario Floreano. 2001. Evolution of adaptive synapses: Robots with fast adaptive behavior in new environments. Evolutionary computation 9, 4 (2001), 495--524."},{"key":"e_1_3_2_1_63_1","doi-asserted-by":"publisher","DOI":"10.1088\/0954-898X_5_3_006"},{"key":"e_1_3_2_1_64_1","doi-asserted-by":"publisher","DOI":"10.1109\/TCDS.2019.2960766"},{"key":"e_1_3_2_1_65_1","volume-title":"Intrinsic neuronal dynamics predict distinct functional roles during working memory. Nature communications 9, 1","author":"Wasmuht Dante Francisco","year":"2018","unstructured":"Dante Francisco Wasmuht, Eelke Spaak, Timothy J Buschman, Earl K Miller, and Mark G Stokes. 2018. Intrinsic neuronal dynamics predict distinct functional roles during working memory. Nature communications 9, 1 (2018), 3499."},{"key":"e_1_3_2_1_66_1","first-page":"15173","article-title":"Supermasks in superposition","volume":"33","author":"Wortsman Mitchell","year":"2020","unstructured":"Mitchell Wortsman, Vivek Ramanujan, Rosanne Liu, Aniruddha Kembhavi, Mohammad Rastegari, Jason Yosinski, and Ali Farhadi. 2020. Supermasks in superposition. Advances in Neural Information Processing Systems 33 (2020), 15173--15184.","journal-title":"Advances in Neural Information Processing Systems"}],"event":{"name":"GECCO '23: Genetic and Evolutionary Computation Conference","sponsor":["SIGEVO ACM Special Interest Group on Genetic and Evolutionary Computation"],"location":"Lisbon Portugal","acronym":"GECCO '23"},"container-title":["Proceedings of the Genetic and Evolutionary Computation Conference"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3583131.3590460","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3583131.3590460","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T16:47:03Z","timestamp":1750178823000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3583131.3590460"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,7,12]]},"references-count":66,"alternative-id":["10.1145\/3583131.3590460","10.1145\/3583131"],"URL":"https:\/\/doi.org\/10.1145\/3583131.3590460","relation":{},"subject":[],"published":{"date-parts":[[2023,7,12]]},"assertion":[{"value":"2023-07-12","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}