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AIJ (Artificial Intelligence Journal)

  • Inza, I., Larrañaga, P., Etxeberria, R. and Sierra, B., 2000. Feature subset selection by Bayesian network-based optimization. Artificial Intelligence, 123(1-2), pp.157-184. { EDA }
  • Jelasity, M. and Dombi, J., 1998. GAS, a concept on modeling species in genetic algorithms. Artificial Intelligence, 99(1), pp.1-19. { GA }
  • Lissovoi, A., Oliveto, P.S. and Warwicker, J.A., 2023. When move acceptance selection hyper-heuristics outperform Metropolis and elitist evolutionary algorithms and when not. Artificial Intelligence, 314, p.103804. [ www ] ( EA )
  • Liu, J., Chen, T., Wang, C., Liang, J., Chen, L., Xiao, Y., Chen, Y. and Jin, K., 2022. VoCSK: Verb-oriented commonsense knowledge mining with taxonomy-guided induction. Artificial Intelligence, 310, p.103744. [ www ] ( SA )
  • Nguyen, P.T.H. and Sudholt, D., 2020. Memetic algorithms outperform evolutionary algorithms in multimodal optimisation. Artificial Intelligence, 287, pp.1-21. [ www ] ( MA )
  • Corus, D., Oliveto, P.S. and Yazdani, D., 2019. Artificial immune systems can find arbitrarily good approximations for the NP-hard number partitioning problem. Artificial Intelligence, 274, pp.180-196. [ www ]
  • Qian, C., Yu, Y., Tang, K., Yao, X. and Zhou Z.H., 2019. Maximizing submodular or monotone approximately submodular functions by multi-objective evolutionary algorithms. Artificial Intelligence, 275, pp.279-294. [ www ]
  • Baldassarre, G. and Nolfi, S., 2009. Strengths and synergies of evolved and designed controllers: A study within collective robotics. Artificial Intelligence, 173(7-8), pp.857-875. [ www ]
  • Watson, J.P., Beck, J.C., Howe, A.E. and Whitley, L.D., 2003. Problem difficulty for tabu search in job-shop scheduling. Artificial Intelligence, 143(2), pp.189-217. [ www ] ( TS )
  • Whitley, D., Rana, S., Dzubera, J. and Mathias, K.E., 1996. Evaluating evolutionary algorithms. Artificial Intelligence, 85(1-2), pp.245-276. [ www ] ( Benchmarking )
  • Reynolds, D. and Gomatam, J., 1996. Stochastic modelling of genetic algorithms. Artificial Intelligence, 82(1-2), pp.303-330. [ www ] ( GA )
  • Reynolds, D. and Gomatam, J., 1996. Similarities and distinctions in sampling strategies for genetic algorithms. Artificial Intelligence, 86(2), pp.375-390. [ www ] ( GA )
  • Battle, D.L. and Vose, M.D., 1993. Isomorphisms of genetic algorithms. Artificial Intelligence, 60(1), pp.155-165. [ www ] ( GA )
  • Bertoni, A. and Dorigo, M., 1993. Implicit parallelism in genetic algorithms. Artificial Intelligence, 61(2), pp.307-314. [ www ] ( GA )
  • Booker, L.B., Goldberg, D.E. and Holland, J.H., 1989. Classifier systems and genetic algorithms. Artificial Intelligence, 40(1-3), pp.235-282. [ www ] ( GA )

2015

Li, M.Q., Yang, S.X. and Liu, X.H., 2015. Bi-goal evolution for many-objective optimization problems. Artificial Intelligence, 228, pp.45-65. [ www ]

Xue, X.S. and Wang, Y.P., 2015. Optimizing ontology alignments through a memetic algorithm using both matchFmeasure and unanimous improvement ratio. Artificial Intelligence, 223, pp.65-81. [ www ]

2013

Bringmann, K. and Friedrich, T., 2013. Approximation quality of the hypervolume indicator. Artificial Intelligence, 195, pp.265-290. [ www ]

Bringmann, K., Friedrich, T., Igel, C. and Voß, T., 2013. Speeding up many-objective optimization by Monte Carlo approximations. Artificial Intelligence, 204, pp.22-29. [ www ]

2012

Yu, Y., Yao, X. and Zhou, Z.H., 2012. On the approximation ability of evolutionary optimization with application to minimum set cover. Artificial Intelligence, 180, pp.20-33. [ www ]

2010

Graff, M. and Poli, R., 2010. Practical performance models of algorithms in evolutionary program induction and other domains. Artificial Intelligence, 174(15), pp.1254-1276. [ www | pdf ]

2008

Yu, Y. and Zhou, Z.H., 2008. A new approach to estimating the expected first hitting time of evolutionary algorithms. Artificial Intelligence, 172(15), pp.1809-1832. [ www | pdf ]

2006

Poli, R. and Langdon, W.B., 2006. Backward-chaining evolutionary algorithms. Artificial Intelligence, 170(11), pp.953-982. [ www | pdf ]

2003

He, J. and Yao, X., 2003. Towards an analytic framework for analysing the computation time of evolutionary algorithms. Artificial Intelligence, 145(1-2), pp.59-97. [ www | pdf ]

2001

He, J. and Yao, X., 2001. Drift analysis and average time complexity of evolutionary algorithms. Artificial Intelligence, 127(1), pp.57-85. [ www | pdf ]

2000

Teller, A. and Veloso, M., 2000. Internal reinforcement in a connectionist genetic programming approach. Artificial Intelligence, 120(2), pp.165-198. [ www | pdf ]

Clearwater, S.H. and Hogg, T., 1996. Problem structure heuristics and scaling behavior for genetic algorithms. Artificial Intelligence, 81(1-2), pp.327-347. [ www | pdf ]