@inproceedings{5ad384ab002a431e8716cfb79b54a782,
title = "Smart Starts: Accelerating Convergence through Uncommon Region Exploration",
abstract = "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{\textquoteright}s superiority in solution quality and convergence speed compared to conventional initialization techniques.",
keywords = "Evolutionary algorithms, empty-space search, initialization, opposition-based learning",
author = "Xinyu Zhang and M{\'a}rio Antunes and Tyler Estro and Erez Zadok and Klaus Mueller",
note = "Publisher Copyright: {\textcopyright} 2025 Copyright held by the owner/author(s).; 2025 Genetic and Evolutionary Computation Conference Companion, GECCO 2025 Companion ; Conference date: 14-07-2025 Through 18-07-2025",
year = "2025",
month = aug,
day = "11",
doi = "10.1145/3712255.3726720",
language = "English",
series = "GECCO 2025 Companion - Proceedings of the 2025 Genetic and Evolutionary Computation Conference Companion",
publisher = "Association for Computing Machinery, Inc",
pages = "547--550",
editor = "Gabriela Ochoa",
booktitle = "GECCO 2025 Companion - Proceedings of the 2025 Genetic and Evolutionary Computation Conference Companion",
}