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Found 7 papers, 6 papers with code
CMA-ES with Learning Rate Adaptation
The results show that the CMA-ES with the proposed learning rate adaptation works well for multimodal and/or noisy problems without extremely expensive learning rate tuning.
Natural Evolution Strategy for Mixed-Integer Black-Box Optimization
This paper proposes a natural evolution strategy (NES) for mixed-integer black-box optimization (MI-BBO) that appears in real-world problems such as hyperparameter optimization of machine learning and materials design.
CMA-ES with Learning Rate Adaptation: Can CMA-ES with Default Population Size Solve Multimodal and Noisy Problems?
The results demonstrate that, when the proposed learning rate adaptation is used, the CMA-ES with default population size works well on multimodal and/or noisy problems, without the need for extremely expensive learning rate tuning.
Fast Moving Natural Evolution Strategy for High-Dimensional Problems
In this work, we propose a new variant of natural evolution strategies (NES) for high-dimensional black-box optimization problems.
Towards a Principled Learning Rate Adaptation for Natural Evolution Strategies
On the other hand, in problems that are difficult to optimize (e. g., multimodal functions), the proposed mechanism makes it possible to set a conservative learning rate when the estimation accuracy of the natural gradient seems to be low, which results in the robust and stable search.
Natural Evolution Strategy for Unconstrained and Implicitly Constrained Problems with Ridge Structure
However, DX-NES-IC has a problem in that the moving speed of the probability distribution is slow on ridge structure.
Natural Policy Gradient Methods with Parameter-based Exploration for Control Tasks
In this paper, we propose an efficient algorithm for estimating the natural policy gradient with parameter-based exploration; this algorithm samples directly in the parameter space.
