Skip to content

Latest commit

 

History

History
61 lines (34 loc) · 8.55 KB

International-Joint-Conference-on-Artificial-Intelligence_IJCAI.md

File metadata and controls

61 lines (34 loc) · 8.55 KB

IJCAI (International Joint Conference on Artificial Intelligence)

  • Abdolmaleki, A., Price, B., Lau, N., Reis, L.P. and Neumann, G., 2017. Contextual covariance matrix adaptation evolutionary strategies. In Proceedings of International Joint Conference on Artificial Intelligence (pp. 1378-1385). [ pdf ]
  • Nannen, V. and Eiben, A.E., 2007, January. Relevance estimation and value calibration of evolutionary algorithm parameters. In Proceedings of International Joint Conference on Artifical Intelligence (pp. 975-980). [ pdf ] ( EDA )
  • Schmidhuber, J., Wierstra, D. and Gomez, F., 2005, July. Evolino: Hybrid neuroevolution/optimal linear search for sequence learning. In Proceedings of International Joint Conference on Artificial Intelligence (pp. 853-858). [ www | pdf ] ( COEA )
  • Gomez, F.J. and Miikkulainen, R., 1999, July. Solving non-Markovian control tasks with neuroevolution. In Proceedings of International Joint Conference on Artificial Intelligence (pp. 1356-1361). [ www | pdf ] ( COEA )
  • Caruana, R.A., Eshelman, L.J. and Schaffer, J.D., 1989, August. Representation and hidden bias II: Eliminating defining length bias in genetic search via shuffle crossover. In Proceedings of International Joint Conference on Artificial Intelligence (pp. 750-755).
  • Koza, J.R., 1989, August. Hierarchical genetic algorithms operating on populations of computer programs. In Proceedings of International Joint Conference on Artificial Intelligence (pp. 768-774). [GP]
  • Smith, S.F., 1983, August. Flexible learning of problem solving heuristics through adaptive search. In Proceedings of International Joint Conference on Artificial Intelligence (pp. 422-425). [ pdf ] ( GA )

2024

Huang, K., Guo, D., Zhang, X., Ji, X. and Liu, H., 2024. CompetEvo: Towards Morphological Evolution from Competition. arXiv preprint arXiv:2405.18300. [ www | pdf ]

Nguyen, D., Le, H., Do, K., Gupta, S., Venkatesh, S. and Tran, T., 2024. Diversifying Training Pool Predictability for Zero-shot Coordination: A Theory of Mind Approach. [ www | pdf ]

Yin, X. and Yang, Y., CMACE: CMAES-based Counterfactual Explanations for Black-box Models. [ www | pdf ]

Chen, D., Zhang, H., Shen, Y., Long, Y. and Shao, L., 2022. Evolutionary Generalized Zero-Shot Learning. arXiv preprint arXiv:2211.13174. [ www | pdf ]

Brunello, A., Geatti, L., Montanari, A. and Saccomanno, N., 2024. Learning What to Monitor: Using Machine Learning to Improve past STL Monitoring. In Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence, IJCAI-24 (pp. 3270-3280). International Joint Conferences on Artificial Intelligence Organization. [ www | pdf ]

Fu, P., Liang, X., Luo, T., Guo, Q., Zhang, Y. and Qian, Y., Core-Structures-Guided Multi-Modal Classification Neural Architecture Search. [ www | pdf ]

Gao, C., de Vazelhes, W., Zhang, H., Gu, B. and Xu, Z., 2024. Hard-Thresholding Meets Evolution Strategies in Reinforcement Learning. arXiv preprint arXiv:2405.01615. [ www | pdf ]

Bian, C., Ren, S., Li, M. and Qian, C., 2024. An Archive Can Bring Provable Speed-ups in Multi-Objective Evolutionary Algorithms. arXiv preprint arXiv:2406.02118. [ www | pdf ]

Guan, X., Yang, T., Zhao, C. and Zhou, Y., Feedback-Based Adaptive Crossover-Rate in Evolutionary Computation. [ www | pdf ]

Lehre, P.K. and Lin, S., 2024. Concentration Tail-Bound Analysis of Coevolutionary and Bandit Learning Algorithms. arXiv preprint arXiv:2405.04480. [ www | pdf ]

Lin, P., Zou, M., Chen, Z. and Cai, S., ParaILP: A Parallel Local Search Framework for Integer Linear Programming with Cooperative Evolution Mechanism. [ www | pdf ]

Ren, S., Qiu, Z., Bian, C., Li, M. and Qian, C., 2024. Maintaining Diversity Provably Helps in Evolutionary Multimodal Optimization. arXiv preprint arXiv:2406.02658. [ www | pdf ]

Zhu, Y., Basu, S. and Pavan, A., 2024. Improved Evolutionary Algorithms for Submodular Maximization with Cost Constraints. arXiv preprint arXiv:2405.05942. [ www | pdf ]

Machado, P., Martins, T., Correia, J., Santo, L.E., Lourenço, N., Cunha, J., Rebelo, S., Martins, P. and Bicker, J., 2024. From Pixels to Metal: AI-Empowered Numismatic Art. In Proceedings of the 33rd International Joint Conference on Artificial Intelligence, IJCAI-24, to appear. International Joint Conferences on Artificial Intelligence Organization. [ www | pdf ]

2020

Liu, F., Li, Z. and Qian, C., 2020, January. Self-guided evolution strategies with historical estimated gradients. In Proceedings of International Joint Conference on Artificial Intelligence (pp. 1474-1480). [ www | pdf ]

2019

Chen, Z., Zhou, Y., He, X. and Jiang, S., 2019, August. A restart-based rank-1 evolution strategy for reinforcement learning. In Proceedings of International Joint Conference on Artificial Intelligence (pp. 2130-2136). [ www | pdf ]

2018

Chrabaszcz, P., Loshchilov, I. and Hutter, F.. Back to basics: Benchmarking canonical evolution strategies for playing Atari. In Proceedings of International Joint Conference on Artificial Intelligence (pp.1419-1426). [ www | pdf | Python ]

Kelly, S. and Heywood, M.I., 2018, July. Emergent tangled program graphs in multi-task learning. In Proceedings of International Joint Conference on Artificial Intelligence (pp. 5294-5298). [ www | pdf ]

Qian, C., Li, G., Feng, C. and Tang, K., 2018, July. Distributed pareto optimization for subset selection. In Proceedings of International Joint Conference on Artificial Intelligence (pp. 1492-1498). [ www | pdf ]

Suganuma, M., Shirakawa, S. and Nagao, T., 2018, July. A genetic programming approach to designing convolutional neural network architectures. In Proceedings of International Joint Conference on Artificial Intelligence (pp. 5369-5373). [ www | pdf | Python ]

  • Schaffer, J.D. and Grefenstette, J.J., 1985, August. Multi-objective learning via genetic algorithms. In Proceedings of International Joint Conference on Artificial Intelligence (pp. 593-595). [ pdf ]

  • Goldberg, D.E., 1985, August. Dynamic system control using rule learning and genetic algorithms. In Proceedings of International Joint Conference on Artificial Intelligence (pp. 588-592). [ pdf ] ( GA )