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The paper introduces TGL-NSGA-II, a teacher-guided fitness approximation framework for expensive evolutionary optimization in constrained TinyML neural architecture search, achieving improved efficiency and reliability over standard methods.
GraphSkillEvo is an evolutionary optimization framework that represents agent skills as graph-structured artifacts to improve LLM performance, outperforming baselines on multiple benchmarks.
Skill-MAS introduces a method for evolving meta-skills in multi-agent systems to improve orchestration without modifying model weights, achieving transferable performance gains across tasks and LLMs.
Introduces a framework combining flow-based generative editing with evolutionary algorithms to perform optimization in residual space, enabling controllable data editing with non-differentiable objectives. Validated on MorphoMNIST and crystal data.
Introduces an automated prompt optimization framework for LLM game agents that decomposes the observation-to-action pipeline into two agents and iteratively refines prompts via an evolutionary loop guided by environment returns. Evaluated on BabyAI tasks, it significantly improves success rates (e.g., from 0% to 72.5% on PutNext) without updating model weights.
DeepEval introduces an evolutionary optimization method for prompts using genetic algorithms, allowing automatic rewriting based on eval feedback and multi-objective optimization.
This paper proposes a neuroevolution-based fine-tuning method to improve the accuracy of quantized deep learning models, showing that nearest-neighbor rounding alone is suboptimal and that evolutionary mutation of weights can yield better results on architectures like VGG and ResNet.