{"ID":2887830,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2508.00380","arxiv_id":"2508.00380","title":"Evolutionary Generative Optimization: Towards Fully Data-Driven Evolutionary Optimization via Generative Learning","abstract":"Recent advances in data-driven evolutionary algorithms (EAs) have demonstrated the potential of leveraging historical data to improve optimization accuracy and adaptability. Despite these advancements, existing methods remain reliant on handcrafted process-level operators. In contrast, Evolutionary Generative Optimization (EvoGO) is a fully data-driven framework designed from the objective level, enabling autonomous learning of the entire search process. EvoGO streamlines the evolutionary optimization process into three stages: data preparation, model training, and population generation. The data preparation stage constructs a pairwise dataset to enrich training diversity without incurring additional evaluation costs. During model training, a tailored generative model learns to transform inferior solutions into superior ones. In the population generation stage, EvoGO replaces traditional reproduction operators with a scalable and parallelizable generative mechanism. Extensive experiments on numerical benchmarks, classical control problems, and high-dimensional robotic tasks demonstrate that EvoGO consistently converges within merely 10 generations and substantially outperforms a wide spectrum of optimization approaches, including traditional EAs, Bayesian optimization, and reinforcement learning based methods. Code is available at: https://github.com/EMI-Group/evogo","short_abstract":"Recent advances in data-driven evolutionary algorithms (EAs) have demonstrated the potential of leveraging historical data to improve optimization accuracy and adaptability. Despite these advancements, existing methods remain reliant on handcrafted process-level operators. In contrast, Evolutionary Generative Optimizat...","url_abs":"https://arxiv.org/abs/2508.00380","url_pdf":"https://arxiv.org/pdf/2508.00380v3","authors":"[\"Tao Jiang\",\"Kebin Sun\",\"Zhenyu Liang\",\"Ran Cheng\",\"Yaochu Jin\",\"Kay Chen Tan\"]","published":"2025-08-01T07:17:57Z","proceeding":"cs.NE","tasks":"[\"cs.NE\"]","methods":"[\"Reinforcement Learning\"]","has_code":false,"code_links":[{"ID":611472,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_id":2887830,"paper_url":"https://arxiv.org/abs/2508.00380","paper_title":"Evolutionary Generative Optimization: Towards Fully Data-Driven Evolutionary Optimization via Generative Learning","repo_url":"https://github.com/EMI-Group/evogo","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}