genetic programming
PulseAugur coverage of genetic programming — every cluster mentioning genetic programming across labs, papers, and developer communities, ranked by signal.
3 day(s) with sentiment data
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New method enhances AI-driven project scheduling heuristics
Researchers have developed a new method using surrogate-assisted genetic programming (GP) to improve heuristic rules for dynamic multi-mode project scheduling. The study focuses on phenotypic characterization (PC) to en…
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LLMs show promise as post-hoc auditors for symbolic regression models
Researchers explored using large language models (LLMs) to audit symbolic regression models for physiological plausibility, particularly in a medical context. While LLMs showed promise in ranking evolved mathematical ex…
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New AI assistant simplifies interpretation of energy consumption models
Researchers have developed an open-source conversational XAI system called the Explainability Assistant, designed to help facility managers and building operators interpret complex machine learning models used for energ…
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New HDL repair system uses dictionary-guided mutations and simulation divergence
Researchers have developed a novel system for automatically repairing Hardware Description Language (HDL) designs, addressing the challenge of large search spaces and strict grammar constraints. The system employs dicti…
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New Neural Symbolic Regression framework combines deep learning with sparse modeling
Researchers have developed a new Neural Symbolic Regression (NSR) framework that combines neural networks with sparse modeling techniques to discover succinct mathematical expressions from data. This approach first uses…
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New ML methods fuse physics and data for better sensitivity analysis
Researchers have developed new physics-informed machine learning strategies to improve global sensitivity analysis (GSA) by effectively combining physics-based models with experimental data. The study explores two machi…
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Agentic AI automates genetic programming configuration
Researchers have developed an agentic AI framework to automate the configuration of parent selection algorithms in genetic programming. This framework utilizes large language model reasoning and retrieval-augmented gene…
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New methods accelerate Vision Transformer adaptation for edge devices
Researchers have developed new methods for adapting Vision Transformers (ViTs) to specific tasks more efficiently. One approach uses genetic programming to evolve layer-specific scalar functions that approximate normali…
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New paper unifies evolutionary computation for autonomous trading signal discovery
A new paper proposes a unified evolutionary computation (EC) perspective on automated formulaic alpha discovery, a process for generating trading signals from symbolic factor spaces. The research introduces a six-compon…
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New Adaptive Nyström Method Enhances Gaussian Process Regression Scalability
Researchers have developed an adaptive Nyström method to improve the scalability of Gaussian Process Regression (GPR). This new approach greedily selects landmark points to minimize approximation errors, outperforming r…
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Genetic Programming guides LLMs for project scheduling
Researchers have developed a novel method to enhance the decision-making capabilities of Large Language Models (LLMs) in dynamic multi-mode project scheduling. This approach involves extracting heuristic knowledge from …
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LLMs show promise in generating genetic programming operators
Researchers have evaluated the ability of eight large language models (LLMs) to generate effective parent-selection operators for genetic programming (GP) in symbolic regression tasks. The study found that models like C…
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China's VC landscape shifts to AI and hard tech in 2026 report
A new report on China's venture capital landscape for 2026 highlights a significant shift in investment focus towards artificial intelligence and hard technology sectors. The report notes a decline in global PE fundrais…
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Bayesian Optimization needs optimal initial points, study finds
A new paper on arXiv explores the optimal number of initial points required for Bayesian Optimization (BO). The research indicates that the total cost of finding a global optimum exhibits a U-shaped relationship with th…
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Heckman correction improves ML model uncertainty calibration
Researchers have developed a new method for addressing epistemic uncertainty in machine learning models, particularly when training data is subject to selection bias. The proposed technique adapts the Heckman correction…
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Australian Government warns of AI scribe privacy risks in GP consultations
The Australian Government has issued a warning regarding the use of AI scribes in medical consultations, highlighting concerns about privacy, consent, and data security. These AI tools, which listen to and transcribe pa…
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Quantum Kernel Bandit Optimization Balances Expressivity and Learnability
Researchers have developed new methods for Gaussian process bandit optimization using quantum kernels, specifically addressing challenges in the noisy intermediate-scale quantum (NISQ) era. The study focuses on balancin…
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Medical AI training data vulnerable to sensitive information leaks
A recent study published in Nature highlights a significant privacy vulnerability in medical AI systems. Researchers discovered that sensitive information, including patient medical records and genetic data, can be extr…
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New ML evaluation metric prioritizes computational effort over accuracy
A new research paper proposes a paradigm shift in evaluating machine learning models, moving beyond maximum accuracy to consider computational effort. The proposed metric, based on the number of gradient descent steps r…
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New research explores genetic programming for symbolic regression · 2 sources tracked
Two recent arXiv papers explore genetic programming (GP) for symbolic regression (SR). One study, "Evaluation of Population Initialization Methods for Genetic Programming-based Symbolic Regression," found that different…