genetic programming
PulseAugur coverage of genetic programming — every cluster mentioning genetic programming across labs, papers, and developer communities, ranked by signal.
5 day(s) with sentiment data
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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…
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Cartesian Genetic Programming runtime analyzed for Boolean functions
A new paper analyzes the runtime of Cartesian Genetic Programming (CGP) when evolving Boolean functions. Researchers established an asymptotic bound of O(n D^5) for CGP to construct a conjunction of n inputs using D bin…
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New Method Confirms Label-Shift Corrections in ML with Limited Data
Researchers have developed a novel method for confirming label-shift corrections in machine learning models, particularly useful in scenarios with limited labeled data. The approach leverages a pre-specified correction …
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Minimalist Genetic Programming offers new approach to program induction
Researchers have introduced Minimalist Genetic Programming (MGP), a novel approach to program induction inspired by linguistic minimalism. Unlike traditional genetic programming that relies on evolutionary search, MGP u…
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U-Net accelerates climate-adaptive urban layout optimization
Researchers have developed a U-Net-based deep learning model to accelerate the optimization of urban layouts for climate adaptation. This approach replaces slow physics simulations with a spatial surrogate model, signif…
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Prostate cancer mortality prediction improved with new AI index
Researchers have developed a new computational framework to create a more accurate comorbidity index for prostate cancer patients. This data-driven approach uses bio-inspired algorithms to recalibrate existing comorbidi…
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AI recalibrates comorbidity index for prostate cancer survival prediction
Researchers have developed a new computational framework to create a more accurate comorbidity index for prostate cancer patients eligible for radical prostatectomy. This data-driven approach uses Population-Based Bio-I…
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New GESR method uses gene editing for faster symbolic regression
Researchers have developed a new symbolic regression method called GESR, which utilizes gene editing inspired by genetic programming. This approach employs two BERT models to intelligently guide mutations and crossovers…