@INPROCEEDINGS{xinyu2025gecco,
  AUTHOR =       "Xinyu Zhang and M\'{a}rio Antunes and Tyler Estro and Erez Zadok and Klaus Mueller",
  TITLE =        "Smart Starts: Accelerating Convergence Through Uncommon Region Exploration",
  YEAR =         "2025",
  ISBN =         "9798400714641",
  PUBLISHER =    "Association for Computing Machinery",
  ADDRESS =      "New York, NY, USA",
  URL =          "https://doi.org/10.1145/3712255.3726720",
  DOI =          "10.1145/3712255.3726720",
  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's superiority in solution quality and convergence speed compared to conventional initialization techniques.",
  BOOKTITLE =    "Proceedings of the Genetic and Evolutionary Computation Conference Companion",
  PAGES =        "547--550",
  NUMPAGES =     "4",
  KEYWORDS =     "evolutionary algorithms, initialization, opposition-based learning, empty-space search",
  LOCATION =     "NH Malaga Hotel, Malaga, Spain",
  SERIES =       "GECCO '25 Companion",
}