# Weighted Random Search for Hyperparameter Optimization

arXiv: https://arxiv.org/abs/2004.01628 · HTML: https://mlsanity.com/paper/2004.01628/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Apr 2020.

## Built on by (most cited first, 6 of 6)

- [Model-predictive control and reinforcement learning in multi-energy system case studies](https://mlsanity.com/paper/2104.09785/) (2021)
- [Visualizing and Explaining Language Models](https://mlsanity.com/paper/2205.10238/) (2022)
- [A self-adapting super-resolution structures framework for automatic design of GAN](https://mlsanity.com/paper/2106.06011/) (2021)
- [Pruning Convolutional Filters via Reinforcement Learning with Entropy Minimization](https://mlsanity.com/paper/2312.04918/) (2023)
- [Preference-Optimal Multi-Metric Weighting for Parallel Coordinate Plots](https://mlsanity.com/paper/2507.02905/) (2025)
- [An Agentic LLM-Based Framework for Population-Scale Mental Health Screening](https://mlsanity.com/paper/2605.13046/) (2026)

## Abstract (arXiv)

We introduce an improved version of Random Search (RS), used here for hyperparameter optimization of machine learning algorithms. Unlike the standard RS, which generates for each trial new values for all hyperparameters, we generate new values for each hyperparameter with a probability of change. The intuition behind our approach is that a value that already triggered a good result is a good candidate for the next step, and should be tested in new combinations of hyperparameter values. Within the same computational budget, our method yields better results than the standard RS. Our theoretical results prove this statement. We test our method on a variation of one of the most commonly used objective function for this class of problems (the Grievank function) and for the hyperparameter optimization of a deep learning CNN architecture. Our results can be generalized to any optimization problem defined on a discrete domain.