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ES-HyperNEAT hyperparameter optimization using TPE shows promise

By PulseAugur Editorial · [2 sources] ·

A new study explores optimizing hyperparameters for ES-HyperNEAT, a neuroevolutionary algorithm, using the Tree-structured Parzen Estimator (TPE) approach. The research investigated over 3 billion hyperparameter combinations for the MNIST classification task, finding that TPE significantly outperformed random search. The best configuration achieved 29.00% accuracy on MNIST, using fewer resources than prior studies. The study also examined the transferability of these optimized hyperparameters to logic operations and the Fashion-MNIST dataset, showing success with Fashion-MNIST but limited transfer to simpler logic tasks. AI

IMPACT This research offers a method to improve the efficiency and effectiveness of neuroevolutionary algorithms, potentially accelerating development in related AI fields.

RANK_REASON The cluster contains an academic paper detailing a novel approach to hyperparameter optimization for a specific AI algorithm.

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

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

ES-HyperNEAT hyperparameter optimization using TPE shows promise

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COVERAGE [2]

  1. arXiv cs.AI TIER_1 English(EN) · Romain Claret, Michael O'Neill, Paul Cotofrei, Kilian Stoffel ·

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

    arXiv:2609.00449v1 Announce Type: cross Abstract: 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 s…

  2. 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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