Population sizing for inductive linkage identification

References

• Arnold , BC , Balakrishnan , N and Nagaraja , HN . 1993 . A First Course in Order Statistics , NY , , USA : John Wiley and Sons .
   Google Scholar
• Baluja, S. (1994), ‘Population-based Incremental Learning: A Method for Integrating Genetic Search Based Function Optimization and Competitive Learning’, Technical report, Carnegie Mellon University, Pittsburgh, PA, USA
   Google Scholar
• Baluja , S and Davies , S . 1997 . Using Optimal Dependency-trees for Combinational Optimization . Proceedings of the Fourteenth International Conference on Machine Learning (ICML-97) . 1997 . pp. 30 – 38 . San Francisco, CA , , USA : Morgan Kaufmann Publishers .
   Google Scholar
• Chen , Y-P , Chuang , C-Y and Huang , Y-W . 2011 . Inductive Linkage Identification on Building Blocks of Different Sizes and Types . International Journal of Systems Science , First published on: 11 April 2011 (iFirst)
   Google Scholar
• Choi , SS , Jung , K and Moon , BR . 2009 . Lower and Upper Bounds for Linkage Discovery . IEEE Transactions on Evolutionary Computation , 13 : 201 – 216 .
   Web of Science ®Google Scholar
• Chuang , CY and Chen , YP . 2007 . Linkage Identification by Perturbation and Decision Tree Induction . Proceedings of 2007 IEEE Congress on Evolutionary Computation (CEC 2007) . 2007 . pp. 357 – 363 .
   Google Scholar
• Cooper , GF and Herskovits , EH . 1992 . A Bayesian Method for the Induction of Probabilistic Networks from Data . Machine Learning , 9 : 309 – 347 .
   Web of Science ®Google Scholar
• de Bonet, J., Isbell, C., and Viola, P. (1997), ‘MIMIC: Finding Optima by Estimating Probability Densities’, in Advances in Neural Information Processing Systems (Vol. 9), Cambridge, MA, USA: The MIT Press, p. 424
   Google Scholar
• Gámez , JA , Mateo , JL and Puerta , JM . 2007 . EDNA: Estimation of Dependency Networks Algorithm . Proceedings of the 2nd International Work-conference on the Interplay Between Natural and Artificial Computation, Part I: Bio-inspired Modeling of Cognitive Tasks . 2007 . pp. 427 – 436 .
   Google Scholar
• Goldberg , DE . 1989 . Genetic Algorithms in Search, Optimization and Machine Learning , Boston, MA , , USA : Addison-Wesley Longman Publishing Co., Inc .
   Google Scholar
• Goldberg , DE , Korb , B and Deb , K . 1989 . Messy Genetic Algorithms: Motivation, Analysis, and First Results . Complex Systems , 3 : 493 – 530 .
   Google Scholar
• Gu , B , Hu , F and Liu , H . 2001 . “ Modelling Classification Performance for Large Data Sets ” . In Advances in Web-aGe Information Management , Edited by: Wang , X. , Yu , G. and Lu , H. 317 – 328 . Berlin/Heidelberg : Springer .
   Google Scholar
• Harik, G.R. (1999), ‘Linkage Learning via Probabilistic Modeling in the ECGA’, IlliGAL Report No. 99010, Illinois Genetic Algorithms Laboratory, University of Illinois at Urbana-Champaign, Urbana, IL, USA
   Google Scholar
• Harik , GR , Lobo , FG and Goldberg , DE . 1999 . The Compact Genetic Algorithm . IEEE Transactions on Evolutionary Computation , 3 : 287 – 297 .
   Web of Science ®Google Scholar
• Harik, G.R. (1997), ‘Learning Gene Linkage to Efficiently Solve Problems of Bounded Difficulty using Genetic Algorithms’, PhD Dissertation, University of Michigan, Ann Arbor, MI, USA
   Google Scholar
• Heckerman , D , Geiger , D and Chickering , DM . 1995 . Learning Bayesian Networks: The Combination of Knowledge and Statistical Data . Machine Learning , 20 : 197 – 243 .
   Web of Science ®Google Scholar
• Jiang , Q , Wang , Y and Yang , XQ . 2009 . A Framework for Estimation of Distribution Algorithms Based on Maximum Entropy . Proceedings of the 5th International Conference on Natural Computation . 2009 . pp. 7 – 11 . Tianjin, China , , Piscataway, NJ, USA : IEEE Press .
   Google Scholar
• Kargupta, H. (1996), ‘SEARCH, Polynomial Complexity, and the Fast Messy Genetic Algorithm’, Ph.D. Dissertation, University of Illinois at Urbana-Champaign, Urbana, IL, USA
   Google Scholar
• Kim , DW , de Silva , CW and Park , GT . 2010 . Evolutionary Design of Sugeno-type Fuzzy Systems for Modelling Humanoid Robots . International Journal of Systems Science , 41 : 875 – 888 .
   Web of Science ®Google Scholar
• Leite , R and Brazdil , P . 2007 . An Iterative Process for Building Learning Curves and Predicting Relative Performance of Classifiers . Proceedings of the Aritficial Intelligence 13th Portuguese Conference on Progress in Artificial Intelligence . 2007 . pp. 87 – 98 . Guimarães, Portugal, Berlin, Heidelberg : Springer-Verlag .
   Google Scholar
• MacKay , DJC . 2003 . Information Theory, Inference and Learning Algorithms , Cambridge : Cambridge University Press .
   Google Scholar
• Morgan , J , Daugherty , R , Hilchie , A and Carey , B . 2003 . Sample Size and Modeling Accuracy of Decision Tree Based Data Mining Tools . Academy of Information and Management Science Journal , 6 : 71 – 99 .
   Google Scholar
• Mühlenbein , H and Höns , R . 2005 . The Estimation of Distributions and the Minimum Relative Entropy Principle . Evolutionary Computation , 13 : 1 – 27 .
   PubMed Web of Science ®Google Scholar
• Mühlenbein , H and Mahnig , T . 1999 . FDA – A Scalable Evolutionary Algorithm for the Optimization of Additively Decomposed Functions . Evolutionary Computation , 7 : 353 – 376 .
   PubMed Web of Science ®Google Scholar
• Munetomo, M., and Goldberg, D. (1998), ‘Identifying Linkage by Nonlinearity Check’, IlliGAL Report No. 98012, Illinois Genetic Algorithms Laboratory, University of Illinois at Urbana-Champaign, Urbana, IL, USA
   Google Scholar
• Munetomo , M and Goldberg , DE . 1999 . Identifying Linkage Groups by Nonlinearity/Non-monotonicity Detection . Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-99) . 1999 . pp. 433 – 440 .
   Google Scholar
• Pelikan , M , Goldberg , DE and Cantú-Paz , E . 1999 . BOA: The Bayesian Optimization Algorithm . Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-99) . 1999 . pp. 525 – 532 .
   Google Scholar
• Pelikan, M., and Mühlenbein, H. (1999), ‘The Bivariate Marginal Distribution Algorithm’, in Advances in Soft Computing – Engineering Design and Manufacturing, Berlin, Germany: Springer-Verlag, pp. 521–535
   Google Scholar
• Pelikan , M , Sastry , K and Goldberg , DE . 2002 . Scalability of the Bayesian Optimization Algorithm . International Journal of Approximate Reasoning , 31 : 221 – 258 .
   Web of Science ®Google Scholar
• Quinlan, J.R. (1993), ‘Induction of Decision Trees’, in Readings in Knowledge Acquisition and Learning: Automating the Construction and Improvement of Expert Systems, San Francisco, CA, USA: Morgan Kaufmann Publishers, pp. 349–361
   Google Scholar
• Rissanen , J . 1978 . Modeling by Shortest Data Description . Automatica , 14 : 465 – 471 .
   Web of Science ®Google Scholar
• Rissanen , J . 1989 . Stochastic Complexity in Statistical Inquiry Theory , River Edge, NJ , , USA : World Scientific Publishing Co .
   Google Scholar
• Rissanen , J . 1996 . Fisher Information and Stochastic Complexity . IEEE Transactions on Information Theory , 1 : 40 – 47 .
   Web of Science ®Google Scholar
• Santana , R , Larrañaga , P and Lozano , JA . 2010 . Learning Factorizations in Estimation of Distribution Algorithms Using Affinity Propagation . Evolutionary Computation , 18 : 515 – 546 .
   PubMed Web of Science ®Google Scholar
• Saridakis , K and Dentsoras , A . 2009 . Integration of Genetic Optimisation and Neuro-fuzzy Approximation in Parametric Engineering Design . International Journal of Systems Science , 40 : 131 – 145 .
   Web of Science ®Google Scholar
• Sastry , K and Goldberg , DE . 2004 . Designing Competent Mutation Operators Via Probabilistic Model Building of Neighborhoods . Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2004) . 2004 . pp. 114 – 125 .
   Google Scholar
• Solteiro Pires , EJ , Tenreiro Machado , JA and de Moura Oliveira , PB . 2006 . Dynamical Modelling of a Genetic Algorithm . Signal Processing , 86 : 2760 – 2770 .
   Web of Science ®Google Scholar
• Streeter , MJ . 2004 . Upper Bounds on the Time and Space Complexity of Optimizing Additively Separable Functions . Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2004) . 2004 . pp. 186 – 197 .
   Google Scholar
• Ting , CK , Zeng , WM and Lin , TC . 2010 . Linkage Discovery Through Data Mining . IEEE Computational Intelligence Magazine , 5 : 10 – 13 .
   Web of Science ®Google Scholar
• Tsuji , M , Munetomo , M and Akama , K . 2006 . Linkage Identification by Fitness Difference Clustering . Evolutionary Computation , 14 : 383 – 409 .
   PubMed Web of Science ®Google Scholar
• Wang , K . 2009 . Application of Genetic Algorithms to Robot Kinematics Calibration . International Journal of Systems Science , 40 : 147 – 153 .
   Web of Science ®Google Scholar
• Yu , TL , Sastry , K , Goldberg , DE and Pelikan , M . 2007 . Population Sizing for Entropy-based Model Building in Discrete Estimation of Distribution Algorithms . Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2007) . 2007 . pp. 601 – 608 .
   Google Scholar
• Zwillinger , D and Kokoska , S . 2000 . CRC Standard Probability and Statistics Tables and Formulae , FL , , USA : CRC Press LLC .
   Google Scholar