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JMLR (Journal of Machine Learning Research)

  • Probst, M. and Rothlauf, F., 2020. Harmless overfitting: Using denoising autoencoders in estimation of distribution algorithms. Journal of Machine Learning Research, 21(78), pp.1-31. [ www | pdf | Matlab ] ( EDA | Combinatorial Optimization )
  • Elsken, T., Metzen, J.H. and Hutter, F., 2019. Neural architecture search: A survey. Journal of Machine Learning Research, 20(55), pp.1-21. [ www | pdf ] ( NE )
  • Wierstra, D., Schaul, T., Glasmachers, T., Sun, Y., Peters, J. and Schmidhuber, J., 2014. Natural evolution strategies. Journal of Machine Learning Research, 15(27), pp.949-980. [ www | pdf | Python - PyBrain | source code ] ( NES | Continuous Optimization )
  • Bergstra, J. and Bengio, Y., 2012. Random search for hyper-parameter optimization. Journal of Machine Learning Research, 13(10), pp.281-305. [ www | pdf ] ( RS )
  • Gomez, F., Schmidhuber, J. and Miikkulainen, R., 2008. Accelerated neural evolution through cooperatively coevolved synapses. Journal of Machine Learning Research, 9(31), pp.937-965. [ www | pdf | C++ ] ( COEA | Continuous Optimization )
    • "Much of the motivation for using the cooperative coevolutionary approach is based on the intuition that many problems may be decomposable into weakly coupled low-dimensional subspaces that can be searched semi-independently by separate species (Wiegand et al., 2001; Jansen and Wiegand, 2003, 2004; Panait et al., 2006). Our experience shows that there may be another, complementary, explanation as to why cooperative coevolution in many cases outperforms single-population algorithms."
  • Panait, L., Tuyls, K. and Luke, S., 2008. Theoretical advantages of lenient learners: An evolutionary game theoretic perspective. Journal of Machine Learning Research, 9(13), pp.423-457. [ www | pdf ] ( COEA | Continuous Optimization )
  • Igel, C., Heidrich-Meisner, V. and Glasmachers, T., 2008. Shark. Journal of Machine Learning Research, 9(33), pp.993-996. [ www | pdf | C++ ] ( ES | Continuous Optimization )

2017

Ollivier, Y., Arnold, L., Auger, A. and Hansen, N., 2017. Information-geometric optimization algorithms: A unifying picture via invariance principles. Journal of Machine Learning Research, 18(18), pp.1-65. [ www | pdf ]

Popovici, E., 2017. Bridging supervised learning and test-based co-optimization. Journal of Machine Learning Research, 18(38), pp.1-39. [ www | pdf ]

2015

Silva, C.P., Dias, D.M., Bentes, C., Pacheco, M.A.C. and Cupertino, L.F., 2015. Evolving GPU machine code. Journal of Machine Learning Research, 16(22), pp.673-712. [ www | pdf ] (Parallel GP on GPU)

Cano, A., Luna, J.M., Zafra, A. and Ventura, S., 2015. A classification module for genetic programming algorithms in JCLEC. Journal of Machine Learning Research, 16, pp.491-494. [ www | pdf ]

Heaton, J., 2015. Encog: Library of interchangeable machine learning models for Java and C#. Journal of Machine Learning Research, 16, pp.1243-1247. [ www | pdf ]

2013

Valsalam, V.K. and Miikkulainen, R., 2013. Using symmetry and evolutionary search to minimize sorting networks. Journal of Machine Learning Research, 14(Feb), pp.303-331. [ www | pdf ]

Salleb-Aouissi, A., Vrain, C., Nortet, C., Kong, X., Rathod, V. and Cassard, D., 2013. QuantMiner for mining quantitative association rules. Journal of Machine Learning Research, 14(1), pp.3153-3157. [ www | pdf ]

Ly, D.L. and Lipson, H., 2012. Learning symbolic representations of hybrid dynamical systems. Journal of Machine Learning Research, 13(1), pp.3585-3618. [ www | pdf ]

Fortin, F.A., De Rainville, F.M., Gardner, M.A.G., Parizeau, M. and Gagné, C., 2012. DEAP: Evolutionary algorithms made easy. Journal of Machine Learning Research, 13(1), pp.2171-2175. [ www | pdf | Python ]

  • Schaul, T., Bayer, J., Wierstra, D., Sun, Y., Felder, M., Sehnke, F., Rückstieß, T. and Schmidhuber, J., 2010. PyBrain. Journal of Machine Learning Research, 11(24), pp.743-746. [ www | pdf | Python ] ( NES )

Verbancsics, P. and Stanley, K.O., 2010. Evolving static representations for task transfer. Journal of Machine Learning Research, 11(58), pp.1737-1769. [ www | pdf ]

2009

Gorissen, D., Dhaene, T. and De Turck, F., 2009. Evolutionary model type selection for global surrogate modeling. Journal of Machine Learning Research, 10(71), pp.2039-2078. [ www | pdf ]

Escalante, H.J., Montes, M. and Sucar, L.E., 2009. Particle swarm model selection. Journal of Machine Learning Research, 10(2). [ www | pdf ]

2006

Whiteson, S. and Stone, P., 2006. Evolutionary function approximation for reinforcement learning. Journal of Machine Learning Research, 7(31), pp.877-917. [ www | pdf ]

2005

Bongard, J. and Lipson, H., 2005. Active coevolutionary learning of deterministic finite automata. Journal of Machine Learning Research, 6(56), pp.1651-1678. [ www | pdf ]

2000

Boyan, J. and Moore, A.W., 2000. Learning evaluation functions to improve optimization by local search. Journal of Machine Learning Research, 1(Nov), pp.77-112. [ www | pdf ]

  • Bull, A.D., 2011. Convergence rates of efficient global optimization algorithms. Journal of Machine Learning Research, 12(88), pp.2879-2904. [ www | pdf ]
  • Moriarty, D.E. and Mikkulainen, R., 1996. Efficient reinforcement learning through symbiotic evolution. Machine Learning, 22(1), pp.11-32. [ www ] ( COEA | Continuous Optimization )
  • Whitley, D., Dominic, S., Das, R. and Anderson, C.W., 1993. Genetic reinforcement learning for neurocontrol problems. Machine Learning, 13, pp.259-284. [ www ] ( GA | Continuous Optimization )
  • Grefenstette, J.J., Ramsey, C.L. and Schultz, A.C., 1990. Learning sequential decision rules using simulation models and competition. Machine Learning, 5(4), pp.355-381. [ www ] ( GA )
  • Grefenstette, J.J., 1988. Credit assignment in rule discovery systems based on genetic algorithms. Machine Learning, 3(2), pp.225-245. [ www ] ( GA )
  • De Jong, K., 1988. Learning with genetic algorithms: An overview. Machine Learning, 3(2-3), pp.121-138. [ www ] ( GA )
  • Goldberg, D.E. and Holland, J.H., 1988. Genetic algorithms and machine learning. Machine Learning, 3(2), pp.95-99. [ www | www ] ( GA )

Pollack, J.B. and Blair, A.D., 1998. Co-evolution in the successful learning of backgammon strategy. Machine Learning, 32(3), pp.225-240. [ www | pdf ]

Forrest, S. and Mitchell, M., 1993. What makes a problem hard for a genetic algorithm? Some anomalous results and their explanation. Machine Learning, 13(2-3), pp.285-319. [ www | pdf ]

Robertson, G.G. and Riolo, R.L., 1988. A tale of two classifier systems. Machine Learning, 3(2), pp.139-159. [ www | pdf ]