Momodou L. Sanyang1,2, Ata Kabán1, Member, IEEE
|  Larrañaga P, Lozano J A. Estimation of Distribution Algorithms:A New Tool for Evolutionary Computation (2002 edition). Kluwer Academic Publishers, 2002.
 Yuan B, Gallagher M. On the importance of diversity maintenance in estimation of distribution algorithms. In Proc. the 2005 Genetic and Evolutionary Computation Conference, June 2005, pp.719-729.
 Friedman J H. An overview of predictive learning and function approximation. In From Statistics to Neural Networks:Theory and Pattern Recognition Applications, Cherkassky V, Friedman J H, Wechsler H (eds.), Springer-Verlag Berlin Heidelberg, 1994, pp.1-61.
 Kabán A, Bootkrajang J, Durrant R J. Toward large-scale continuous EDA:A random matrix theory perspective. Evolutionary Computation, 2016, 24(2):255-291.
 Dong W, Yao X. Covariance matrix repairing in Gaussian based EDAs. In Proc. the 2007 IEEE Congress on Evolutionary Computation, September 2007, pp.415-422.
 Paul T K, Iba H. Linear and combinatorial optimizations by estimation of distribution algorithms. In Proc. the 9th MPS Symposium on Evolutionary Computation, Jan. 2002, pp.99-106.
 Ros R, Hansen N. A simple modification in CMA-ES achieving linear time and space complexity. In Proc. the 10th International Conference on Parallel Problem Solving from Nature, September 2008, pp.296-305.
 Bosman P. On empirical memory design, faster selection of Bayesian factorizations and parameter-free Gaussian EDAs. In Proc. the 2009 Genetic and Evolutionary Computation Conference, July 2009, pp.389-396.
 de Bonet J S, Isbell C L, Viola P. MIMIC:Finding optima by estimating probability densities. In Proc. the 1997 International Conference on Neural Information Processing Systems, December 1996, pp.424-430.
 Bosman P A N, Thierens D. An algorithmic framework for density estimation based evolutionary algorithms. Technical Report, Utrecht University, 1999. https://homepages.cwi.nl/~bosman/publications/1999analgorithmicframework.pdf, June 2019.
 Armañanzas R, Inza I, Santana R et al. A review of estimation of distribution algorithms in bioinformatics. BioData Mining, 2008, 1:Article No. 6.
 Weicker K, Weicker N. On the improvement of coevolutionary optimizers by learning variable interdependencies. In Proc. the 1999 Congress on Evolutionary Computation, July 1999, pp.1627-1632.
 Dong W, Chen T, Tiňo P, Yao X. Scaling up estimation of distribution algorithm for continuous optimisation. IEEE Transaction of Evolutionary Computation, 2013, 17(6):797-822.
 Yang Z, Tang K, Yao X. Multilevel cooperative convolution for large scale optimization. In Proc. IEEE World Congress on Computational Intelligence, June 2008, pp.1663-1670.
 Molina D, Lozano M, Sánchez A M, Herrera F. Memetic algorithms based on local search chains for large scale continuous optimisation problems:MA-SSW-Chains. Soft Computing, 2011, 15(11):2201-2220.
 Shin S Y, Cho D Y, Zhang B T. Function optimization with latent variable models. In Proc. the 3rd International Symposium on Adaptive Systems, March 2001, pp.145-152.
 Sanyang M L, Kabán A. REMEDA:Random embedding EDA for optimising functions with intrinsic dimension. In Proc. the 14th International Conference on Parallel Problem Solving from Nature, September 2016, pp.859-868.
 Sanyang M L, Kabán A. Heavy tails with parameter adaptation in random projection based continuous EDA. In Proc. the 2015 IEEE Congress on Evolutionary Computation, May 2015, pp.2074-2081.
 Yao X, Liu Y, Lin G. Evolutionary programming made faster. IEEE Transaction on Evolutionary Computation, 1999, 3(2):82-102.
 Gao B, Wood I. TAM-EDA:Multivariate t distribution, archive and mutation based estimation of distribution algorithm. ANZIAM Journal, 2014, 54:720-746.
 Gao B. Estimation of distribution algorithms for singleand multi-objective optimization[Ph.D. Thesis]. School of Mathematics and Physics, The University of Queensland, 2014.
 Sanyang M L, Durrant R J, Kabán A. How effective is Cauchy-EDA in high dimensions? In Proc. the 2016 IEEE Congress on Evolutionary Computation, July 2016, pp.3409-3416.
 Dang D C, Lehre P K, Nguyen P T H. Level-based analysis of the univariate marginal distribution algorithm. Algorithmica, 2018, 81(2):668-702.
 Kabán A. On compressive ensemble induced regularisation:How close is the finite ensemble precision matrix to the infinite ensemble? In Proc. the 2017 International Conference on Algorithmic Learning Theory, October 2017, pp.617-628.
 Kabán A. New bounds for compressive linear least squares regression. In Proc. the 17th International Conference on Artificial Intelligence and Statistics, April 2014, pp.448-456.
 Achlioptas D. Database-friendly random projections:Johnson-Lindenstrauss with binary coins. Journal of Computer and System Sciences, 2003, 66(4):671-687.
 Grahl J, Bosman P A, Rothlauf F. The correlation-triggered adaptive variance scaling IDEA. In Proc. the 2006 Genetic and Evolutionary Computation Conference, July 2006, pp.397-404.
 Eiben A E, Hinterding R, Michalewicz Z. Parameter control in evolutionary algorithms. IEEE Transaction on Evolutionary Computation, 1999, 3(2):124-141.
 Wong Y Y, Lee K H, Leung K S, Ho C W. A novel approach in parameter adaptation and diversity maintenance for genetic algorithms. Soft Computing, 2003, 7(8):506-515.
 Beyer H G, Schwefel H P. Evolution strategies-A comprehensive introduction. Natural Computing, 1999, 1(1):3-52.
 Tang K, Li X D, Suganthan P N, Yang Z, Weise T. Benchmark functions for the CEC'2010 special session and competition on large scale global optimization. Technical report, Nature Inspired Computation and Applications Laboratory, 2012. http://www.tflsgo.org/assets/cec2018/cec2-013-lsgo-benchmark-tech-report.pdf, June 2019.
 Abramowitz M, Stegun I A, Morse P M (eds.). Handbook of Mathematical Functions with Formulas, Graphs and Mathematical Tables. National Bureau of Standards, 1964.
|||Concha Bielza, Juan A. Fernández del Pozo, and Pedro Larrañaga. Parameter Control of Genetic Algorithms by Learning and Simulation of Bayesian Networks —— A Case Study for the Optimal Ordering of Tables [J]. , 2013, 28(4): 720-731.|
|||Nan Ding, Shu-De Zhou, and Zeng-Qi Sun. Histogram-Based Estimation of Distribution Algorithm: A Competent Method for Continuous Optimization [J]. , 2008, 23(1): 35-43 .|