### The correlation-triggered adaptive variance scaling IDEA

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[1] DAVID G. KENDALL,et al. Introduction to Mathematical Statistics , 1947, Nature.

[2] T. W. Anderson,et al. An Introduction to Multivariate Statistical Analysis , 1959 .

[3] T. Gerig. Multivariate Analysis: Techniques for Educational and Psychological Research , 1975 .

[4] R. Bargmann,et al. Multivariate Analysis (Techniques for Educational and Psychological Research) , 1989 .

[5] H. Mühlenbein,et al. From Recombination of Genes to the Estimation of Distributions I. Binary Parameters , 1996, PPSN.

[6] Lothar Thiele,et al. A Comparison of Selection Schemes Used in Evolutionary Algorithms , 1996, Evolutionary Computation.

[7] Louise Travé-Massuyès,et al. Telephone Network Traffic Overloading Diagnosis and Evolutionary Computation Techniques , 1997, Artificial Evolution.

[8] Michèle Sebag,et al. Extending Population-Based Incremental Learning to Continuous Search Spaces , 1998, PPSN.

[9] Heinz Mühlenbein,et al. FDA -A Scalable Evolutionary Algorithm for the Optimization of Additively Decomposed Functions , 1999, Evolutionary Computation.

[10] Marcus Gallagher,et al. Real-valued Evolutionary Optimization using a Flexible Probability Density Estimator , 1999, GECCO.

[11] Pedro Larrañaga,et al. Optimization in Continuous Domains by Learning and Simulation of Gaussian Networks , 2000 .

[12] Dirk Thierens,et al. Expanding from Discrete to Continuous Estimation of Distribution Algorithms: The IDEA , 2000, PPSN.

[13] J. A. Lozano,et al. Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation , 2001 .

[14] Nikolaus Hansen,et al. Completely Derandomized Self-Adaptation in Evolution Strategies , 2001, Evolutionary Computation.

[15] Dirk Thierens,et al. Advancing continuous IDEAs with mixture distributions and factorization selection metrics , 2001 .

[16] Pedro Larrañaga,et al. Estimation of Distribution Algorithms , 2002, Genetic Algorithms and Evolutionary Computation.

[17] David E. Goldberg,et al. A Survey of Optimization by Building and Using Probabilistic Models , 2002, Comput. Optim. Appl..

[18] Pedro Larrañaga,et al. Mathematical modelling of UMDAc algorithm with tournament selection. Behaviour on linear and quadratic functions , 2002, Int. J. Approx. Reason..

[19] Peter A. N. Bosman,et al. Design and Application of iterated Density-Estimation Evolutionary Algorithms , 2003 .

[20] Petros Koumoutsakos,et al. Reducing the Time Complexity of the Derandomized Evolution Strategy with Covariance Matrix Adaptation (CMA-ES) , 2003, Evolutionary Computation.

[21] David E. Goldberg,et al. Real-Coded Bayesian Optimization Algorithm: Bringing the Strength of BOA into the Continuous World , 2004, GECCO.

[22] Petros Koumoutsakos,et al. A Mixed Bayesian Optimization Algorithm with Variance Adaptation , 2004, PPSN.

[23] Petros Koumoutsakos,et al. Learning probability distributions in continuous evolutionary algorithms – a comparative review , 2004, Natural Computing.

[24] Anja Vogler,et al. An Introduction to Multivariate Statistical Analysis , 2004 .

[25] Franz Rothlauf,et al. Behaviour of UMDA/sub c/ with truncation selection on monotonous functions , 2005, 2005 IEEE Congress on Evolutionary Computation.

[26] Marcus Gallagher,et al. On the importance of diversity maintenance in estimation of distribution algorithms , 2005, GECCO '05.

[27] Dirk Thierens,et al. Learning Probabilistic Models for Enhanced Evolutionary Computation , 2005 .

[28] Peter A. N. Bosman,et al. Matching inductive search bias and problem structure in continuous Estimation-of-Distribution Algorithms , 2008, Eur. J. Oper. Res..