# Space Net Optimization

arXiv: https://arxiv.org/abs/2306.00043 · HTML: https://mlsanity.com/paper/2306.00043/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 28 references that are in the map.
- First posted on arXiv 31 May 2023.

## Builds on (most cited first, 3 of 3)

- [Optuna: A Next-generation Hyperparameter Optimization Framework](https://mlsanity.com/paper/1907.10902/) (2019)
- [On Hyperparameter Optimization of Machine Learning Algorithms: Theory and Practice](https://mlsanity.com/paper/2007.15745/) (2020)
- [A Survey on Evolutionary Neural Architecture Search](https://mlsanity.com/paper/2008.10937/) (2020)

## Abstract (arXiv)

Most metaheuristic algorithms rely on a few searched solutions to guide later searches during the convergence process for a simple reason: the limited computing resource of a computer makes it impossible to retain all the searched solutions. This also reveals that each search of most metaheuristic algorithms is just like a ballpark guess. To help address this issue, we present a novel metaheuristic algorithm called space net optimization (SNO). It is equipped with a new mechanism called space net; thus, making it possible for a metaheuristic algorithm to use most information provided by all searched solutions to depict the landscape of the solution space. With the space net, a metaheuristic algorithm is kind of like having a ``vision'' on the solution space. Simulation results show that SNO outperforms all the other metaheuristic algorithms compared in this study for a set of well-known single objective bound constrained problems in most cases.