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neuroevolution

topic2 events
papersSEP 10 04:00 UTC

TPE-based hyperparameter optimization and transferability study for ES-HyperNEAT

A new arXiv paper applies a Tree-structured Parzen Estimator method to tune the hyperparameters of ES-HyperNEAT, a neuroevolution technique for growing neural networks. The work analyzes both how effectively this optimization improves results and whether the best settings transfer across problems. The authors note that NEAT-family methods remain highly sensitive to hyperparameter choices.

neuroevolution/hyperparameter-optimization/research
papersSEP 10 04:00 UTC

New arXiv Paper Introduces JAX-ESHN, a GPU-Parallel ES-HyperNEAT Implementation

A new arXiv preprint describes JAX-ESHN, a rewrite of the ES-HyperNEAT neuroevolution method using JAX to run whole populations on GPUs. The authors note that, to their knowledge, no existing implementation of the algorithm had offered this level of parallelism before. ES-HyperNEAT itself develops neural network layouts by adaptively subdividing a quadtree, and the new implementation is meant to make it faster and more practical on modern hardware.

neuroevolution/jax/gpu/hyperneat