# Smart Starts: Accelerating Convergence through Uncommon Region Exploration

arXiv: https://arxiv.org/abs/2505.05661 · HTML: https://mlsanity.com/paper/2505.05661/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 May 2025.

## Built on by (most cited first, 1 of 1)

- [Efficient On-Policy Reinforcement Learning via Exploration of Sparse Parameter Space](https://mlsanity.com/paper/2509.25876/) (2025)

## Abstract (arXiv)

Initialization profoundly affects evolutionary algorithm (EA) efficacy by dictating search trajectories and convergence. This study introduces a hybrid initialization strategy combining empty-space search algorithm (ESA) and opposition-based learning (OBL). OBL initially generates a diverse population, subsequently augmented by ESA, which identifies under-explored regions. This synergy enhances population diversity, accelerates convergence, and improves EA performance on complex, high-dimensional optimization problems. Benchmark results demonstrate the proposed method's superiority in solution quality and convergence speed compared to conventional initialization techniques.