7 articles tagged with #genetic-algorithms. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.
Researchers developed ERIS, a new framework that uses genetic algorithms to exploit Audio Large Models (ALMs) by disguising malicious instructions as natural speech with background noise. The system can bypass safety filters by embedding harmful content in real-world audio interference that appears harmless to humans and security systems.
Researchers developed a multi-agent simulation framework combining Large Language Models and Genetic Algorithms to study how social media users evolve language strategies to evade platform moderation policies. The study demonstrates that evasion tactics become more sophisticated over iterative exchanges, with validated real-world relevance through user studies.
Researchers present a mathematical framework for genetic algorithms that employ ML-guided mutation and recombination operators instead of random transformations, modeling the approach as a query-complexity problem. The work demonstrates that certain optimization problems require all three components—generation, mutation, and recombination—to be solved efficiently, with solution diversity playing a critical role in practical performance.
Researchers have developed a hybrid approach combining Wasserstein GANs with Genetic Algorithms to improve synthetic graph generation by refining structural properties like degree and spectral distributions. The method reduces deviations from real-world graphs while preserving diversity, advancing generative models for realistic graph synthesis and data augmentation applications.
Researchers have enhanced sieving algorithms for solving the Shortest Vector Problem (SVP) in lattices by applying domain-informed representation and genetic algorithm approaches, with extensions to module lattices. This advancement strengthens post-quantum cryptographic foundations designed to resist attacks from future quantum computers.
Researchers demonstrate that recombination-based operators in Cartesian Genetic Programming can achieve competitive performance when combined with proper hyperparameter optimization, challenging the long-held assumption that mutation-only approaches are superior for symbolic regression tasks.
Researchers have developed a new white-box watermarking framework that uses chaotic sequences to embed ownership information into deep neural network parameters for intellectual property protection. The method uses logistic maps and genetic algorithms to verify model ownership without degrading performance, showing effectiveness on MNIST and CIFAR-10 datasets.
