Introduced MO-SB-NESR method to improve physical consistency in multi-output symbolic regression.

Manuel Rodriguez

This study evaluates eight large language models (LLMs) in zero-shot generation of parent selection operators for symbolic regression in genetic programming, demonstrating competitive performance.

Hengzhe Zhang, Qi Chen, Bing Xue et al.

Proposes DoYouRemember architecture combining VQ-VAE, LoRA-tuned LLM, and diffusion decoder for image memory reconstruction.

Xuguang Yu, Weigang Zheng, Minyue Yu

MMAO: bio-inspired self-adaptive optimizer using endogenous resource loops for continuous and discrete problems.

Jinliang Xu, Liping Ma

Proposes Bézier Walk Evolution (BWE), integrating geometry-driven path construction with distance-aware random walks, balancing exploration and exploitation via adaptive curve order.

Jinpeng Wang, Xingguo Xu, Yujing Sun et al.

EvoFlock employs multi-objective genetic algorithms to automatically optimize 15 parameters of multi-agent flocking models, achieving behaviors aligned with user-defined metrics.

Craig Reynolds

Genetic algorithm optimization of reservoir hyperparameters (size, spectral radius, etc.) reveals structural constraints that enhance spatiotemporal chaos prediction, extending forecast horizon and efficiency.

Nima Dehghani

This paper provides the first runtime analysis of Cartesian Genetic Programming (CGP) in evolving Boolean functions, establishing bounds of O(nD^5) for conjunctions and exponential time for XOR, highlighting the impact of selection strategies.

Duc-Cuong Dang, Roman Kalkreuth, Andre Opris

Proposes IG-DOE, a large language model-assisted cooperative operator ensemble evolution algorithm, integrating multi-operator switching to significantly improve permutation flow shop scheduling performance.

Rui Xu, Yufan Liao, Haoze Lv et al.

MeEvo combines natural evolution and metacognitive reflection through cyclic alternation, significantly improving search stability and solution quality on complex optimization tasks.

Zishang Qiu, Xinan Chen, Rong Qu et al.

Proposes a co-evolutionary SNN ensemble framework based on marginal contribution fitness, significantly improving multi-task performance.

Catherine Rodriquez, James Ghawaly

SES framework optimizes symbolic solutions directly from equations without training data, successfully recovering algebraic and differential equations' explicit expressions.

Sergei Garmaev, Vinay Sharma, Olga Fink

Proposes Hub-Aware hybrid search combining pre-processing and likelihood-pheromone guidance to enhance cosmic web filament detection efficiency.

Simone Vilardi, Reynier Peletier, Felipe Contreras et al.

This paper introduces GP-RV-GOMEA, a hybrid model-based evolutionary algorithm that simultaneously optimizes symbolic expression structures and real-valued constants, achieving significant accuracy improvements.

Johannes Koch, Tanja Alderliesten, Peter A. N. Bosman

Memristor-based analog SNN accelerator reduces energy consumption by 12.7× and delay by 1.26×, enabling real-time edge intelligence for bio-inspired interception tasks.

Qianhou Qu, Sheng Lu, Liuting Shang et al.

Introduces a multi-solution optimization framework for the Linear Ordering Problem (LOP) based on recent economic data, leveraging advanced metaheuristics to enhance solution diversity and quality.

Fabrizio Fagiolo, Marco Baioletti, Valentino Santucci

Proposed Quantum Genetic Negative Selection Algorithm (QGNSA) achieves superior anomaly detection accuracy on Metaverse Financial Transactions Dataset.

Giancarlo P. Gamberi, Calebe P. Bianchini

Introduced the first systematic fairness benchmark for SNNs, revealing impacts of bias and hardware constraints on model performance.

Hudi He, Fukun Wang, Zhe Wang et al.

Study shows architecture choice crucial for symbolic regression target recovery using EML operator.

Chakshu Gupta

The Structure-Guided Diffusion Model (SGDM) integrates structural information to enhance EEG-based visual reconstruction fidelity.

Yongxiang Lian, Yueyang Cang, Pingge Hu et al.