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ES-HyperNEAT hyperparameter optimization shows transferability on MNIST

By PulseAugur Editorial · [1 sources] ·

Researchers have investigated hyperparameter optimization for the ES-HyperNEAT algorithm using the Tree-structured Parzen Estimator (TPE) approach. Applied to the MNIST classification task, TPE successfully navigated over 3 billion potential combinations, achieving a 29.00% accuracy with fewer generations and a smaller population size than previous studies. The optimized hyperparameters demonstrated transferability to the Fashion-MNIST dataset, though their effectiveness was limited on simpler logic operations. AI

IMPACT Provides a method for optimizing neuroevolutionary algorithms and insights into hyperparameter transferability across tasks.

RANK_REASON Academic paper published on arXiv detailing a novel approach to hyperparameter optimization for a neuroevolutionary algorithm. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.NE (Neural & Evolutionary) →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

ES-HyperNEAT hyperparameter optimization shows transferability on MNIST

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Academic paper published on arXiv detailing a novel approach to hyperparameter optimization for a neuroevolutionary algorithm. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.NE (Neural & Evolutionary) TIER_1 English(EN) · Kilian Stoffel ·

    Investigating Hyperparameter Optimization and Transferability for ES-HyperNEAT: A TPE Approach

    Neuroevolution of Augmenting Topologies (NEAT) and its advanced version, Evolvable-Substrate HyperNEAT (ES-HyperNEAT), have shown great potential in developing neural networks. However, their effectiveness heavily depends on the selection of hyperparameters. This study investigat…

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