Statistical Linkage Learning enables a new mask construction algorithm for Partition Crossover that maintains effectiveness on noisy problems with hidden dependencies and matches noise-free performance when decomposition quality is high.
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LyMPuS is a parameterless on-the-fly surrogate enabling perfect monotonic comparisons and guaranteed linkage discovery in at most 2 log n steps for non-linear problems.
citing papers explorer
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Obtaining Partition Crossover masks using Statistical Linkage Learning for solving noised optimization problems with hidden variable dependency structure
Statistical Linkage Learning enables a new mask construction algorithm for Partition Crossover that maintains effectiveness on noisy problems with hidden dependencies and matches noise-free performance when decomposition quality is high.
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Limited Perfect Monotonical Surrogates constructed using low-cost recursive linkage discovery with guaranteed output
LyMPuS is a parameterless on-the-fly surrogate enabling perfect monotonic comparisons and guaranteed linkage discovery in at most 2 log n steps for non-linear problems.