GraViti introduces a graph-level VAE with relaxed permutation invariance that maps whole graphs to latent vectors, achieves strong reconstruction on large molecular datasets, and generates valid samples by learning constraints directly from graph-level representations.
Completely derandomized self-adaptation in evolution strategies.Evolutionary Computation, 9(2):159–195
2 Pith papers cite this work. Polarity classification is still indexing.
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RCMAES augments CMA-ES with nonlinear dimension-dependent population sizing and adaptive restarts, delivering competitive results on CEC2017, CEC2020, and CEC2022 benchmarks.
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GraViti: Graph-Level Variational Autoencoders with Relaxed Permutation Invariance
GraViti introduces a graph-level VAE with relaxed permutation invariance that maps whole graphs to latent vectors, achieves strong reconstruction on large molecular datasets, and generates valid samples by learning constraints directly from graph-level representations.
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RCMAES: A Robust CMA-ES Variant for CEC2026 Competition
RCMAES augments CMA-ES with nonlinear dimension-dependent population sizing and adaptive restarts, delivering competitive results on CEC2017, CEC2020, and CEC2022 benchmarks.