genetic programming
PulseAugur coverage of genetic programming — every cluster mentioning genetic programming across labs, papers, and developer communities, ranked by signal.
6 day(s) with sentiment data
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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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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…
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Researchers evolve activation functions to handle missing data in neural networks
Researchers have developed a novel approach called Three-Channel Evolved Activations (3C-EA) to address challenges in machine learning when dealing with missing data. Unlike traditional activation functions, 3C-EA incor…
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EvoTSC evolves lightweight models for time series classification
Researchers have developed EvoTSC, a new genetic programming approach to automatically create efficient models for time series classification. This method incorporates expert knowledge into the evolutionary process to g…