AlgoEvo is introduced, a unified agentic architecture that transforms automated algorithm discovery into an interactive, knowledge-accumulating process, demonstrating strong intra-task accumulation, cross-task transfer, and the ability to reproduce or exceed the strongest existing methods through flexible skill activat...
Jun-Hao Qiu, Qing-Long Hu, Ji Cheng et al.· 0 citations
Large language models have advanced automated algorithm discovery by synthesizing executable code, but existing frameworks trap them in rigid search pipelines with pre-defined control flows. This limitation restricts adaptive reasoning, blocks cross-paradigm transfer, and discards valuable execution feedback. We propos...
Jun-Hao Qiu, Qing-Long Hu, Xia-Liang Tong et al.· 0 citations
DyCA treats instance clustering as a co-evolving component within the search process, reusing accumulated evaluation data as feature-free signals to progressively partition instances with similar algorithmic response patterns, thereby enabling finer-grained and more adaptive guidance for specialized algorithm design.
Qinglong Hu, Qingfu Zhang, Fei Liu et al.· 0 citations
This work proposes a general LES framework that incorporates feature-assisted niche construction within abstract search spaces, enabling the seamless integration of niche-based search strategies from evolutionary computation and introduces PartEvo (Partition to Evolve), an LES method that combines niche collaborative s...
Qinglong Hu, Qingfu Zhang· Neural Information Processin...· 10 citations· ⚡3
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