# Enhancing Optimization Performance: A Novel Hybridization of Gaussian Crunching Search and Powell's Method for Derivative-Free Optimization

arXiv: https://arxiv.org/abs/2308.04649 · HTML: https://mlsanity.com/paper/2308.04649/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 1 of its 2 references that are in the map.
- First posted on arXiv 9 Aug 2023.

## Builds on (most cited first, 1 of 1)

- [A new derivative-free optimization method: Gaussian Crunching Search](https://mlsanity.com/paper/2307.14359/) (2023)

## Abstract (arXiv)

This research paper presents a novel approach to enhance optimization performance through the hybridization of Gaussian Crunching Search (GCS) and Powell's Method for derivative-free optimization. While GCS has shown promise in overcoming challenges faced by traditional derivative-free optimization methods [1], it may not always excel in finding the local minimum. On the other hand, some traditional methods may have better performance in this regard. However, GCS demonstrates its strength in escaping the trap of local minima and approaching the global minima. Through experimentation, we discovered that by combining GCS with certain traditional derivative-free optimization methods, we can significantly boost performance while retaining the respective advantages of each method. This hybrid approach opens up new possibilities for optimizing complex systems and finding optimal solutions in a range of applications.