Skip to content

Latest commit

 

History

History
31 lines (19 loc) · 4.63 KB

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

File metadata and controls

31 lines (19 loc) · 4.63 KB
Raw

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 )

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 )