Evolutionary Algorithms and Applications

Últimos artículos

• G\"odel Forest: Balancing Search Depth and Breadth for Data-Centric Recursive Self-Improvement

  Ziqi Zhao, Fanqing Meng, Haocheng Lu, Lingxiao Du, Qiguang Chen, Mengkang Hu, Xiao-Ming Wu · 1 de octubre de 2026

  Recursive self-improvement (RSI) aims to achieve compounding gains by having models improve themselves. While most existing RSI systems optimize external agent harnesses or prompts around a frozen base model, data-centric RSI directly updates the model's own parameters by training on agent-generated…

• Adaptive-GEPA: Make Your Harness Fit Heterogeneous Requests

  Tianyu Chen, Yasi Zhang, Ruiyi Wang, Xinran Zhao, Taoran Li, Mingyuan Zhou · 1 de octubre de 2026

  Reflective optimizers such as GEPA improve language model prompts from execution traces and evaluator feedback; full-program extensions can also rewrite tools and control flow. In practice, a user hands the same endpoint heterogeneous requests whose effective solutions require different tools, reaso…

• Rep2Skill: Representation-Guided Skill Self-Evolution for LLM Agents

  Kaixing Zhang, Changming Li, Yingdong Shi, Zheng Zhang, Kaitao Song, Wenjie Shi, Jingang Wang, Kan Ren · 1 de octubre de 2026

  Textual skills enable large language model (LLM) based agents to accumulate reusable procedural knowledge without updating model parameters. Yet existing skill evolution remains largely confined to the text space: an optimizer must diagnose success and failure patterns, and revise skills solely from…

• Do Self-Evolving Skills Generalize to Held-Out Tasks?

  Xihao Piao, Zifeng Wang, Zhen Chen · 1 de octubre de 2026

  AI agents can externalize what they learn from past tasks into reusable \emph{skills}, such as procedures, checklists, code, or other executable artifacts, that can be retrieved and reused when solving new tasks. Self-evolving skill methods keep rewriting these skills after each round of practice on…

• Self-Evolving Algorithm-Design Agents: Escaping In-Context Evolutionary Stagnation via Population-Curated Policy Optimization

  Chen Lu, Ke Xue, Siyuan Xu, Mingxuan Yuan, Chao Qian · 1 de octubre de 2026

  Large language models are increasingly participating in complex real-world tasks in the form of algorithm-design agents, designing and refining algorithms. Many successful algorithm-design agents adopt pure in-context evolutionary frameworks, but they may quickly plateau in domains that require spec…

• Component-Aware Feedback for Self-Evolving Programs

  Ethan Lin, Jinming Nian, Yi Fang · 1 de octubre de 2026

  LLM-guided evolutionary search can discover complex programs, but existing methods mostly only save candidate programs and fitness scores while discarding which component edits produced which fitness metric changes. Existing methods force the mutator LLM to infer the effect of prior edits from clutt…

• Self-Evolving Harness on Multiple Tasks with the Agent as Its Own Optimizer

  Qiankai Xu · 1 de octubre de 2026

  A harness is the code around a language-model agent that organizes prompts, calls tools, manages context, and controls execution. As models grow stronger, recent work has begun to let agents improve their own harnesses, a line of work known as self-evolving harnesses. In most existing methods, a sep…

• Evolving Towards Better Codes: LLM-Guided Search for High-Distance Binary Linear Codes

  Amal Seddas, Vladyslav Shashkov, Maryna Viazovska, Emmanuel Abbe · 30 de septiembre de 2026

  Evolutionary program search driven by large language models (LLMs) has produced record-breaking constructions for open problems in combinatorics and beyond. We apply this approach to the longstanding problem of improving the best-known bounds for binary linear codes. Building on the EvoTune evolutio…

• Mixture of Self-Improving Branches For Agent Harness Optimization

  Haoyu Dong, Yuhang Zhou, Zihao Lin, Yifan Wu, Bo Peng, Mingyi Wang, Xiangjun Fan, Lizhu Zhang, Zhuokai Zhao · 30 de septiembre de 2026

  Harness optimization provides a practical setting for recursive self-improvement (RSI), where agent-generated modifications inform subsequent changes through execution feedback. Recent work such as Meta-Harness implements this process through iterative code generation and evaluation, but retains a f…

• How Can Recommendation Feedback Evolve Agent Memory?

  Shanwen Mao, Mingming Li, Hao Zhang, Zhiheng Li, Yige Wang, Penghua Yu, Junxiong Zhu · 30 de septiembre de 2026

  Content-generation agents continuously receive impressions, clicks, conversions, and negative feedback from recommendation systems, providing real-world outcome signals for memory evolution. However, these signals are delayed and noisy, confounded by audience composition, placement, and recommendati…

• SimpleEvol: An Agent-Loop Framework for LLM-Driven Automated Heuristic Design with Minimal Human Priors

  Jianghan Zhu, Cong Zhang, Rongjie Zhu, Chi Zhang, Zhiguang Cao · 30 de septiembre de 2026

  Large language models (LLMs) have emerged as powerful tools for automated heuristic design (AHD), enabling iterative generation and refinement of heuristics. However, the dominant paradigm embeds LLMs as narrow, fixed components, such as crossover or mutation, within heavily hand-engineered evolutio…

• SAGE: A Statistical Acceptance Gate for Self-Evolving Agents

  Yihao Wang, Linhan Xia, Rui Liu, Zhaofeng Zhang, Hongyu Wu, Yang Yang, Jinglu He, Yu Guo, Kai Lei · 30 de septiembre de 2026

  Large Language Model (LLM)-based agents increasingly self-evolve by editing a persistent skill document that encodes their workflow, tool-use rules, and decision logic. This loop has two steps, an optimizer that proposes a candidate edit and a gate that accepts or rejects it. Prior work has concentr…

• Direct Self-Evolving Optimization: Evolving LLMs without Challenger Training

  Yuyang Deng, Yu Wang, Jiayun Wang · 29 de septiembre de 2026

  Self-evolving language models improve by generating tasks and learning from their own feedback, but adapting the task generator often requires a separate challenger-training loop. Can we generate tasks adapted to the current solver without explicitly training a challenger? We introduce \textbf{D}ire…

• JET: Judge-Guided Evolution at Test Time for Agent Programs

  Yao Long Teng, Jiayi Cai, Bo An · 29 de septiembre de 2026

  An agent's executable program governs how it uses tools, processes observations, and responds to failures. Evolving this program at test time can help adaptation, but deciding which changes to retain is difficult when true rewards are unavailable. Execution traces provide evidence of agent behavior,…

• Which Self-Improvements Should We Trust? Reliable Self-Improvement When Agents Reuse Their Benchmarks

  Xiaojing Sun, Yuhan Zeng, Zihua She, Xiao Wang · 29 de septiembre de 2026

  As recursive self-improvement (RSI) rapidly advances, reliable evaluation becomes critical for guiding adaptive search. RSI typically relies on finite evaluation resources, such as fixed benchmarks, to determine which modifications are retained and what is proposed next. However, when these finite r…

• Not Every Term Adds New Structure: Sobolev Novelty for Symbolic Regression

  Boxiao Wang, Kai Li, Yuheng Jing, Tianyi Liu, Chen Li, Yifan Zhang, Jian Cheng · 29 de septiembre de 2026

  Symbolic regression (SR) aims to discover compact and meaningful mathematical equations from data, but searching the vast combinatorial space of symbolic structures remains challenging. Existing methods typically guide this process using expression-level objectives, such as fitting error, which asse…

• SNIP++: Fine-Grained Symbolic-Numerical Alignment for Symbolic Regression

  Benjamin L\'eger, Shubham Gupta, Samy Mammeri, Kazem Meidani, Cem Subakan, Christian Gagn\'e · 29 de septiembre de 2026

  Mathematical expressions and the numerical behavior they produce are two views of the same underlying function, and connecting them is central to scientific discovery. Symbolic Regression (SR) relies on this connection directly: it searches for an expression that reproduces a given behavior. Recent …

• MechBench: Can AI Scientific Agents Discover Mechanisms Beyond Phenomenal Laws?

  Zihan Yu, Jiadong Zhang, Jialin Cheng, Jingtao Ding, Yong Li · 29 de septiembre de 2026

  Scientific discovery requires not only recovering mathematical laws that describe observable behavior, but also identifying the mechanisms that generate them. Existing benchmarks for symbolic regression and scientific agents primarily evaluate phenomenal-law recovery, leaving mechanism discovery lar…

• SRHarness: A Harness for Agentic Symbolic Regression

  Zihan Yu, Shixuan Zhou, Hao Huang, Jingtao Ding, Yong Li · 29 de septiembre de 2026

  Recent agentic symbolic regression approaches increasingly rely on large language models to analyze data, select scientific operations, and refine hypotheses over long search trajectories. In such systems, performance depends not only on the underlying model and search strategy, but also on the runt…

• Environmental requirements for the use of social information by artificial life agents using evolved plastic artificial neural networks

  Hugh Charterton, James M. Borg, Aniko Ekart · 29 de septiembre de 2026

  Evolved Plastic Artificial Neural Networks (EPANNs) consist of two principal processes, the first, evolution, and the second, development and in-life learning. In the context of the origins of social = learning, very few studies have been carried out using ALIFE models based on EPANN requirements. S…

• Self-Evolving Agents via Likelihood-Guided Tool-Space Optimization

  Xuanqi Zhang, Ruinan Jin, Running Yang, Yuxuan Zhang, Minghui Chen, Wenlong Deng, Xiaoxiao Li · 29 de septiembre de 2026

  Self-evolving agents can continually improve their behavior, while tools define the executable action space through which they interact with the environment. However, exposing the full tool library to model introduces substantial irrelevant context and can impair tool-use decisions. We study tool-sp…

• Towards Certificate-Driven Software Porting: A Self-Improving Agentic Harness for Scientific Program Optimization

  Piyush Jha, Aishik Ghosh, Vijay Ganesh · 29 de septiembre de 2026

  The upgrade and rewriting of large scientific codebases has traditionally been a major challenge. While evolutionary search with large language models (LLMs) can port and accelerate legacy code, repair feedback in prompts alone does not prevent subsequent candidates from repeating the same errors. W…

• EverMine: Dissecting the Self-Evolution of Research Capabilities in Long-Horizon Alpha Research

  Siyuan Li, Jiangfeng Zhang, Rui Yao, Weihua Qiu, Mingyang Xu, Zixuan Yuan · 29 de septiembre de 2026

  Self-evolving agents aim to turn research feedback into reusable skills, tools, and research rules. Whether these accumulated capabilities continue to improve later research requires controlled evaluation. Long-horizon alpha discovery provides a state-dependent setting: once a new factor enters the …

• COEVO: Co-Evolving Context and Parameters for Recursive Self-Improvement

  Siwei Chen, Xinping Bao, Xinyu Cai, Yuan Cao, Wan Jiang, Shaohong Chen · 29 de septiembre de 2026

  Recursive self-improvement (RSI) seeks to move large language models beyond static training pipelines toward systems that can participate in improving their own future behavior. Existing approaches largely follow two directions: updating model parameters through online learning, or improving the ext…

• Certified Long-Horizon Code Agent Evolution via Validation-Gated Skill Optimization

  Yifan Wang, Hao Cheng, Xiaomin Li, Yuexing Hao, Hemanth Neelgund Ramesh, Dongwon Jung, Hao Tang, Keru Wang, Chenliang Zhou, Qianhui Wu, Wenlin Yao, Ananth Grama, Andrzej Banburski-Fahey, Baolin Peng, Jaron Lanier, Jianfeng Gao · 29 de septiembre de 2026

  Long horizon agent self-evolution without model weight updates is essential for enabling deployed agents to accumulate reusable skills and improve over time. Prior self-evolution work has focused primarily on short-horizon tasks, while repository-level software engineering remains unexplored despite…