| The surprising creativity of digital evolution: A collection of anecdotes from the evolutionary computation and artificial life research communities J Lehman, J Clune, D Misevic, C Adami, L Altenberg, J Beaulieu, ... Artificial life 26 (2), 274-306, 2020 | 153 | 2020 |
| Neural modularity helps organisms evolve to learn new skills without forgetting old skills KO Ellefsen, JB Mouret, J Clune PLoS computational biology 11 (4), e1004128, 2015 | 129 | 2015 |
| Multiobjective coverage path planning: Enabling automated inspection of complex, real-world structures KO Ellefsen, HA Lepikson, JC Albiez Applied Soft Computing 61, 264-282, 2017 | 19 | 2017 |
| Combining MAP-elites and incremental evolution to generate gaits for a mammalian quadruped robot J Nordmoen, KO Ellefsen, K Glette International Conference on the Applications of Evolutionary Computation …, 2018 | 15 | 2018 |
| Evolved embodied phase coordination enables robust quadruped robot locomotion J Nordmoen, TF Nygaard, KO Ellefsen, K Glette Proceedings of the Genetic and Evolutionary Computation Conference, 133-141, 2019 | 13 | 2019 |
| Dynamic mutation in MAP-Elites for robotic repertoire generation J Nordmoen, E Samuelsen, KO Ellefsen, K Glette ALIFE 2018: The 2018 Conference on Artificial Life, 598-605, 2018 | 10 | 2018 |
| Evolved sensitive periods in learning K Ellefsen Artificial Life Conference Proceedings 13, 409-416, 2013 | 10 | 2013 |
| Planning inspection paths through evolutionary multi-objective optimization KO Ellefsen, HA Lepikson, JC Albiez Proceedings of the Genetic and Evolutionary Computation Conference 2016, 893-900, 2016 | 9 | 2016 |
| Balancing the costs and benefits of learning ability K Ellefsen Artificial Life Conference Proceedings 13, 292-299, 2013 | 9 | 2013 |
| How do mixture density rnns predict the future? KO Ellefsen, CP Martin, J Torresen arXiv preprint arXiv:1901.07859, 2019 | 8 | 2019 |
| Experiences from real-world evolution with dyret: Dynamic robot for embodied testing TF Nygaard, J Nordmoen, KO Ellefsen, CP Martin, J Tørresen, K Glette Symposium of the Norwegian AI Society, 58-68, 2019 | 6 | 2019 |
| Deep predictive models in interactive music CP Martin, KO Ellefsen, J Torresen arXiv preprint arXiv:1801.10492, 2018 | 6 | 2018 |
| The evolution of learning under environmental variability K Ellefsen Artificial Life Conference Proceedings 14, 649-656, 2014 | 5 | 2014 |
| Guiding neuroevolution with structural objectives KO Ellefsen, J Huizinga, J Torresen Evolutionary computation 28 (1), 115-140, 2020 | 3 | 2020 |
| Deep models for ensemble touch-screen improvisation CP Martin, KO Ellefsen, J Torresen Proceedings of the 12th International Audio Mostly Conference on Augmented …, 2017 | 3 | 2017 |
| MAP-Elites enables Powerful Stepping Stones and Diversity for Modular Robotics J Nordmoen, F Veenstra, KO Ellefsen, K Glette Frontiers in Robotics and AI 8, 2021 | 2 | 2021 |
| Dynamic robot scheduling using a genetic algorithm KO Ellefsen 2011 IEEE Congress of Evolutionary Computation (CEC), 2025-2032, 2011 | 2 | 2011 |
| Quality and diversity in evolutionary modular robotics J Nordmoen, F Veenstra, KO Ellefsen, K Glette 2020 IEEE Symposium Series on Computational Intelligence (SSCI), 2109-2116, 2020 | 1 | 2020 |
| Self-Adapting Goals Allow Transfer of Predictive Models to New Tasks KO Ellefsen, J Torresen Symposium of the Norwegian AI Society, 28-39, 2019 | 1 | 2019 |
| Evolving neural networks with multiple internal models KO Ellefsen, J Tørresen Artificial Life Conference Proceedings 14, 138-145, 2017 | 1 | 2017 |
Kyrre GletteDepartment of Informatics, RITMO, University of OsloVerified email at ifi.uio.no
