For NeurIPS, we try to collect all the papers related to Evolutionary Computation.
Fontaine, M.C. and Nikolaidis, S., 2021. Differentiable quality diversity. In Advances in Neural Information Processing Systems. [ www | openreview | pdf | ppt | Python ] ( QD )
Bhatia, J.S., Jackson, H., Tian, Y., Xu, J. and Matusik, W., 2021. Evolution Gym: A large-scale benchmark for evolving soft robots. In Advances in Neural Information Processing Systems (pp. 2201-2214). Curran Associates, Inc. [ www | pdf | Python | https://evolutiongym.github.io/ ] ( GA/CPPN/NEAT for ER )
Parker-Holder, J., Pacchiano, A., Choromanski, K.M. and Roberts, S.J., 2020. Effective diversity in population based reinforcement learning. In Advances in Neural Information Processing Systems. [ www | pdf | Python ]
Najarro, E. and Risi, S., 2020. Meta-learning through hebbian plasticity in random networks. In Advances in Neural Information Processing Systems. [ www | pdf | Python ]
Lee, K., Lee, B.U., Shin, U. and Kweon, I.S., 2020. An efficient asynchronous method for integrating evolutionary and gradient-based policy search. In Advances in Neural Information Processing Systems. [ www | pdf | Python ]
Parker-Holder, J., Nguyen, V. and Roberts, S.J., 2020. Provably efficient online hyperparameter optimization with population-based bandits. In Advances in Neural Information Processing Systems. [ www | pdf | Python ]
Confavreux, B., Zenke, F., Agnes, E.J., Lillicrap, T. and Vogels, T.P., 2020. A meta-learning approach to (re)discover plasticity rules that carve a desired function into a neural network. In Advances in Neural Information Processing Systems. [ www | pdf ]
Barbalau, A., Cosma, A., Ionescu, R.T. and Popescu, M., 2020. Black-box ripper: Copying black-box models using generative evolutionary algorithms. In Advances in Neural Information Processing Systems. [ www | pdf | Python ]
Ahn, S.S., Kim, J., Lee, H. and Shin, J., 2020. Guiding deep molecular optimization with genetic exploration. In Advances in Neural Information Processing Systems. [ www | pdf | Python ]
Etcheverry, M., Moulin-Frier, C. and Oudeyer, P.Y., 2020. Hierarchically organized latent modules for exploratory search in morphogenetic systems. In Advances in Neural Information Processing Systems. [ www | pdf | Python ]
Liu, H., Brock, A., Simonyan, K. and Le, Q.V., 2020. Evolving normalization-activation layers. In Advances in Neural Information Processing Systems. [ www | pdf | Python ]
Choromanski, K., Pacchiano, A., Parker-Holder, J. and Tang, Y., 2019. From complexity to simplicity: Adaptive es-active subspaces for blackbox optimization. In Advances in Neural Information Processing Systems. [ www | pdf | Python ]
Cao, Y., Chen, T., Wang, Z. and Shen, Y., 2019. Learning to optimize in swarms. In Advances in Neural Information Processing Systems. [ www | pdf | C++ ]
- Ha, D. and Schmidhuber, J., 2018, December. Recurrent world models facilitate policy evolution. In Advances in Neural Information Processing Systems (pp. 2455-2467). [ www | pdf | Python | worldmodels.github.io ] ( CMA-ES | ER )
Conti, E., Madhavan, V., Such, F.P., Lehman, J., Stanley, K.O. and Clune, J., 2018. Improving exploration in evolution strategies for deep reinforcement learning via a population of novelty-seeking agents. In Advances in Neural Information Processing Systems. [ www | pdf | Python ]
Houthooft, R., Chen, R.Y., Isola, P., Stadie, B.C., Wolski, F., Ho, J. and Abbeel, P., 2018. Evolved policy gradients. In Advances in Neural Information Processing Systems. [ www | pdf | Python | blog ]
Khadka, S. and Tumer, K., 2018. Evolution-guided policy gradient in reinforcement learning. In Advances in Neural Information Processing Systems. [ www | pdf | Python ]
Chang, S., Yang, J., Choi, J. and Kwak, N., 2018. Genetic-gated networks for deep reinforcement learning. In Advances in Neural Information Processing Systems. [ www | pdf ]
Cui, X., Zhang, W., Tüske, Z. and Picheny, M., Evolutionary stochastic gradient descent for optimization of deep neural networks. In Advances in Neural Information Processing Systems. [ www |pdf | Python ]
Krause, O., Arbonès, D.R. and Igel, C., 2016. CMA-ES with optimal covariance update and storage complexity. In Advances in Neural Information Processing Systems, 29, pp.370-378. [ www | pdf ]
Qian, C., Yu, Y. and Zhou, Z.H., 2015. Subset selection by Pareto optimization. In Advances in Neural Information Processing Systems, 28, pp.1774-1782. [ www | pdf ]
Baluja, S., 1996. Genetic algorithms and explicit search statistics. In Advances in Neural Information Processing Systems, pp.319-325. [ www | pdf ]
Juels, A. and Wattenberg, M., 1995. Stochastic hillclimbing as a baseline method for evaluating genetic algorithms. In Advances in Neural Information Processing Systems (pp. 430-436). MIT Press. [ www | pdf ]
Mitchell, M., Holland, J. and Forrest, S., 1993. When will a genetic algorithm outperform hill climbing. In Advances in Neural Information Processing Systems (pp. 51-58). Morgan-Kaufmann. [ www | pdf ]