Genetic Programming
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Multi-Modal Learning meets Genetic Programming: Analyzing Alignment in Latent Space Optimization
arXiv:2604.08324v3 Announce Type: replace Abstract: Symbolic regression (SR) aims to discover mathematical expressions from data, a task traditionally tackled using Genetic Programming (GP) through combinatorial search over symbolic structures. Latent Space Optimization (LSO) methods use neural encoders to map symbolic expressions into continuous spaces, transforming the combinatorial search into continuous optimization. SNIP (Meidani et al., 2024), a contrastive pre-training model inspired...
Simultaneous Model-Based Evolution of Constants and Expression Structure in GP-GOMEA for Symbolic Regression
arXiv:2606.02236v1 Announce Type: new Abstract: Genetic programming (GP) approaches are among the state-of-the-art for symbolic regression, the task of constructing symbolic expressions that fit well with data. To find highly accurate symbolic expressions, both the expression structure and any contained real-valued constants, are important. GP-GOMEA, a modern model-based evolutionary algorithm, is one of the leading algorithms for finding accurate, yet compact expressions.
AlphaEval: A Comprehensive and Efficient Evaluation Framework for Formula Alpha Mining
Announce Type: replace Abstract: Formula alpha mining, which generates predictive signals from financial data, is critical for quantitative investment. Although various algorithmic approaches-such as genetic programming, reinforcement learning, and large language models-have significantly expanded the capacity for alpha discovery, systematic evaluation remains a key challenge. Existing evaluation metrics predominantly include backtesting and correlation-based measures.
A unified developmental framework of the human placenta in its uterine environment in vivo and in vitro
Despite advances in single-cell profiling of the human placenta, the genetic programs governing its physiological remodeling throughout gestation remain incompletely understood; this limits the interpretation of trophoblast organoid models. Here, we reconstruct the human placenta in its uterine environment across gestation by integrating public single-cell data into a unified developmental framework developed through a specialized computational strategy. We resolve 100 cell subtypes,...
Breaking the Simplification Bottleneck in Amortized Neural Symbolic Regression
Announce Type: replace Abstract: Symbolic regression (SR) aims to discover interpretable analytical expressions that accurately describe observed data. Amortized SR promises to be much more efficient than the predominant genetic programming SR methods, but currently struggles to scale to realistic scientific complexity. We find that a key obstacle is the lack of a fast reduction of equivalent expressions to a concise normalized form.
Decomposable Neuro Symbolic Regression
Announce Type: replace Abstract: Symbolic regression (SR) models complex systems by discovering mathematical expressions that capture underlying relationships in observed data. However, most SR methods prioritize minimizing prediction error over identifying the governing equations, often producing overly complex or inaccurate expressions. To address this, we present a decomposable SR method that generates interpretable multivariate expressions leveraging transformer models, genetic...
Learning Randomized Reductions
arXiv:2412.18134v4 Announce Type: replace Abstract: Randomized self-reductions (RSRs) express $f(x)$ using $f$ evaluated at random correlated points, enabling self-correcting programs, instance-hiding protocols, and applications in complexity theory and cryptography. Yet discovering RSRs has required manual expert derivation for over 40 years, limiting their practical use. We present Bitween for automated RSR learning.
Learning Randomized Reductions
arXiv:2412.18134v5 Announce Type: replace Abstract: Randomized self-reductions (RSRs) express $f(x)$ using $f$ evaluated at random correlated points, enabling self-correcting programs, instance-hiding protocols, and applications in complexity theory and cryptography. Yet discovering RSRs has required manual expert derivation for over 40 years, limiting their practical use. We present Bitween for automated RSR learning.
These California wildflowers could save other plants
These California wildflowers could save other plants Lisa Lock Scientific Editor Andrew Zinin Lead Editor As wildflowers go, the mountain jewelflower is demure, clever and quietly unbreakable. It has spread across many of California's iconic landscapes, from Sonoma wine country to the oak-dotted foothills, even over the Sierra Crest, where snow covers the ground during winter. "It seems at first glance like it could grow just about anywhere," said Jennifer Gremer, an associate professor in...
Embryos shape their limbs: a key discovery of "genetic brakes"
Canadian scientists have made a significant advance in understanding the mechanisms that enable embryos to properly form their limbs, thanks to new research led by Université de Montréal medical professor Marie Kmita at the Montreal Clinical Research Institute (IRCM). In findings published in the journal PNAS, Kmita and her team highlight the crucial role of certain molecular systems that act as true “genetic brakes,” ensuring that development proceeds correctly. At the very beginning of...