MetaSG-SAEA is a bi-level meta-BBO framework that uses a meta-policy for search guidance via the MM-CCI constraint abstraction and diffusion-based population initialization to outperform baselines on expensive constrained multi-objective optimization problems.
IEEE Transactions on Evolutionary Computation , year=
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EvoOR-Agent co-evolves agent architectures as AOE-style networks with graph-mediated recombination and knowledge-base-assisted mutation to outperform fixed LLM pipelines on OR benchmarks.
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Meta-Black-Box Optimization Can Do Search Guidance for Expensive Constrained Multi-Objective Optimization
MetaSG-SAEA is a bi-level meta-BBO framework that uses a meta-policy for search guidance via the MM-CCI constraint abstraction and diffusion-based population initialization to outperform baselines on expensive constrained multi-objective optimization problems.
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Co-evolving Agent Architectures and Interpretable Reasoning for Automated Optimization
EvoOR-Agent co-evolves agent architectures as AOE-style networks with graph-mediated recombination and knowledge-base-assisted mutation to outperform fixed LLM pipelines on OR benchmarks.