The correlation-triggered adaptive variance scaling IDEA
Proceedings of the 8th annual conference on Genetic and evolutionary computation
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This article arguments that rank correlation coefficients are powerful association measures and how can they be adopted by EDAs. A new EDA implements the proposed ideas: the Non-Parametric Real-valued Estimation Distribution Algorithm (NOPREDA). The paper fully describes the rank correlation coefficient, and the procedure to build a non parametric model for the probability distribution of the source data. A benchmark of global optimization problems is solved with NOPREDA.