Learning Nonlinear Predictive Models for Lossless Image Compression
We describe a genetic programming system which learns nonlinear predictive models for lossless image compression.
genome-complier↗
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Understanding Climate-Vegetation Interactions in Global Rainforests Through a GP-Tree Analysis
The tropical rainforests are the largest reserves of terrestrial carbon and, therefore, the future of these rainforests is a question that is of immense importance in the geoscience research community. With the recent severe Amazonian droughts in 2005 and 2010 and on-going drought in the Congo region for more than two decades, there is growing concern that these forests could succumb to precipitation reduction, causing extensive carbon release and feedback to the carbon cycle. However, there is no single ecosystem model that quantifies the relationship between vegetation health in these rainforests and climatic factors. Small scale studies have used statistical correlation measure and simple linear regression to model climate-vegetation interactions, but suffer from the lack of comprehensive data representation as well as simplistic assumptions about dependency of the target on the covariates. In this paper we use genetic programming (GP) based symbolic regression for discovering equations that govern the vegetation climate dynamics in the rainforests. Expecting micro-regions within the rainforests to have unique characteristics compared to the overall general characteristics, we use a modified regression-tree based hierarchical partitioning of the space to build individual models for each partition. The discovery of these equations reveal very interesting characteristics about the Amazon and the Congo rainforests. Our method GP-tree shows that the rainforests exhibit tremendous resiliency in the face of extreme climatic events by adapting to changing conditions.
genetic programming↗
An interactive machine learning platform for analyzing multi-particle coincidence data from cold target recoil ion momentum spectroscopy
We present SCULPT (Supervised Clustering and Uncovering Latent Patterns with Training), a comprehensive software platform for analyzing tabulated high-dimensional multi-particle coincidence data from Cold Target Recoil Ion Momentum Spectroscopy (COLTRIMS) experiments. The software addresses critical challenges in modern momentum spectroscopy by integrating advanced machine learning techniques with physics-informed analysis in an interactive web-based environment. SCULPT implements uniform manifold approximation and projection for non-linear dimensionality reduction to reveal correlations in high-dimensional data. We also discuss potential extensions to deep autoencoders for feature learning and genetic programming for automated discovery of physically meaningful observables. A novel adaptive confidence scoring system provides quantitative reliability assessments by evaluating user-selected clustering quality metrics with predefined weights that reflect each metric’s robustness. The platform features configurable molecular profiles for different experimental systems, interactive visualization with selection tools, and comprehensive data filtering capabilities. Utilizing a subset of SCULPT’s capabilities, we analyze photo-double-ionization data measured using the COLTRIMS method for three-body dissociation of the D 2 O molecule, revealing distinct fragmentation channels and their correlations with physics parameters. The software’s modular architecture and web-based implementation make it accessible to the broader atomic and molecular physics community, significantly reducing the time required for complex multi-dimensional analyses. This opens the door to finding and isolating rare events exhibiting non-linear correlations on the fly during experimental measurements, which can help steer exploration and improve the efficiency of experiments.
Artificial neural networks↗
Analytical simulation of meander morphology from equilibrium to long-term evolution: Impacts of channel geometry and vegetation-induced coarsening
This study introduces an innovative approach to modeling meandering river morphology, integrating and investigating the effects of geometric characteristics and vegetation-induced channel coarsening. The developed comprehensive framework combines several advanced techniques: Genetic Programming for refining the scour factor of transverse bed slope, a Leaf Area Index (LAI)-enhanced analytical model for quantifying vegetative flow resistance, and an upstream-weighted moving average method for efficient approximation of the convolution integral in meander migration calculations. The model is validated against both an idealized Kinoshita meander and a natural bend of the Tumen River (China) in equilibrium, demonstrating its robustness across diverse scales and conditions. The model's ability to simulate the long-term evolution, including cutoff events, provides valuable insight for river management strategies. The current findings demonstrate that channel geometry, particularly width-to-depth ratio, plays a dominant role in meander evolution, with wider channels prone to more complex and rapid morphological changes. Vegetation effects are most pronounced in channels with moderate width-to-depth ratios, where they can significantly influence migration rates and bed topography. A combination of channel widening and deepening, coupled with strategic vegetation management, can effectively enhance navigability while maintaining channel stability in the studied Tumen River reach. Sensitivity analyses highlight the complex interplay between hydraulic conditions, sediment characteristics, and vegetation in shaping river morphology. This research advances understanding of the multifaceted nature of meandering river systems and offers practical tools for informed decision-making in river engineering and environmental management, particularly in the context of climate change and increasing anthropogenic pressures on fluvial ecosystems.
54 ENVIRONMENTAL SCIENCES↗
Models and molecular approaches to assessing the effects of the microgravity environment on vertebrate development
The extent to which gravity, and especially the lack thereof, can affect normal development in higher organisms is poorly understood. Underlying this question is the assumption that normal development depends on the embryo's ability to maintain a programmed temporal and spatial coordination of morphogenetic events. There are several reports documenting the apparently normal development of several vertebrate species, including mammals, under conditions of exposure to space flight during various periods of the development process. Evidence to the contrary also exists and it is therefore likely that some alterations in morphology do occur in a microgravity environment. Although subsequent development may appear overtly normal, more subtle abnormalities result. In all studies, the evaluation is restricted by the few numbers of specimens that can be examined and the relatively insensitive techniques for assessing potentially subtle effects. In the present discussion, we summarize some observations of mammalian development made in microgravity and consider which stages might be expected to be differentially sensitive to altered gravity conditions. While we emphasize mammalian development, we discuss the suitability of another model system for examining such effects in a cross-species context. Furthermore, we consider recent developments in our understanding of the molecular genetic program regulating embryogenesis that could serve as markers for assessing perturbations of development.
Cosmos 1129 Project↗
Rational Modulation of Plant Root Development Using Engineered Cytokinin Regulators
Achieving precise control over quantitative developmental phenotypes is a key objective in plant biology. Recent advances in synthetic biology have enabled tools to reprogram entire developmental pathways; however, the complexity of designing synthetic genetic programs and the inherent interactions between various signaling processes remains a critical challenge. Here, we leverage Type-B response regulators to modulate the expression of genes involved in cytokinin-dependent growth and development processes. We rationally engineered these regulators to modulate their transcriptional activity (i.e., repression or activation) and potency while reducing their sensitivity to cytokinin. By localizing the expression of these engineered transcription factors using tissue-specific promoters, we can predictably tune cytokinin-regulated traits. As a proof of principle, we deployed this synthetic system in Arabidopsis thaliana to either decrease or increase the number of lateral roots. The simplicity and modularity of our approach makes it an ideal system for controlling other developmental phenotypes of agronomic interest in plants.
Cell signaling↗
Stress intensity factor models using mechanics-guided decomposition and symbolic regression
The finite element method can be used to compute accurate stress intensity factors (SIFs) for cracks with complex geometries and boundary conditions. In contrast, handbook solutions act as surrogate SIF models that provide significantly faster evaluation times. However, the development of conventional surrogate SIF models relies on manual development based on low-order parameterizations. This limits surrogate model accuracy and generalizability. Here, in this paper, we develop a framework for the automated development of mechanics-guided handbook SIF solutions by using interpretable machine learning via genetic programming for symbolic regression (GPSR). Formalizing the mechanics-based approach of Raju and Newman, SIF training data is decomposed into multiple subsets. This decomposition enables parallel GPSR model development of subfunctions, each of which accounts for specific geometrical corrections with respect to a known analytical model. Using this mechanics-based approach with GPSR allows for equations to be learned with improved accuracy and reduced complexity relative to the Raju Newman equations while maintaining the inherent interpretability of mathematical expressions. In this paper, we present equations that match the complexity of the Raju Newman equations while having reduced error, as well as equations with similar errors and reduced complexity.
42 ENGINEERING↗
SymbolNet: neural symbolic regression with adaptive dynamic pruning for compression
Abstract Compact symbolic expressions have been shown to be more efficient than neural network (NN) models in terms of resource consumption and inference speed when implemented on custom hardware such as field-programmable gate arrays (FPGAs), while maintaining comparable accuracy (Tsoi et al 2024 EPJ Web Conf. 295 09036). These capabilities are highly valuable in environments with stringent computational resource constraints, such as high-energy physics experiments at the CERN Large Hadron Collider. However, finding compact expressions for high-dimensional datasets remains challenging due to the inherent limitations of genetic programming (GP), the search algorithm of most symbolic regression (SR) methods. Contrary to GP, the NN approach to SR offers scalability to high-dimensional inputs and leverages gradient methods for faster equation searching. Common ways of constraining expression complexity often involve multistage pruning with fine-tuning, which can result in significant performance loss. In this work, we propose S y m b o l N e t , a NN approach to SR specifically designed as a model compression technique, aimed at enabling low-latency inference for high-dimensional inputs on custom hardware such as FPGAs. This framework allows dynamic pruning of model weights, input features, and mathematical operators in a single training process, where both training loss and expression complexity are optimized simultaneously. We introduce a sparsity regularization term for each pruning type, which can adaptively adjust its strength, leading to convergence at a target sparsity ratio. Unlike most existing SR methods that struggle with datasets containing more than O ( 10 ) inputs, we demonstrate the effectiveness of our model on the LHC jet tagging task (16 inputs), MNIST (784 inputs), and SVHN (3072 inputs).
Tsoi, Ho Fung (ORCID:0000000225502184)↗
SCULPT (Supervised Clustering and Uncovering Latent Patterns with Training) v1
SCULPT (Supervised Clustering and Uncovering Latent Patterns with Training) is a comprehensive data visualization and analysis application focused on working with COLTRIMS (COLd Target Recoil Ion Momentum Spectroscopy) data, which is used in atomic and molecular physics experiments. The application offers several powerful features: - Data uploading and processing capabilities for COLTRIMS files - Multiple visualization methods using UMAP (Uniform Manifold Approximation and Projection) for dimensionality reduction - Interactive selection of data points across multiple views - Feature engineering through various methods: - Manual feature selection from calculated physics parameters - Deep autoencoder for dimension reduction - Genetic programming for discovering meaningful features - Mutual information-based feature selection - Multiple clustering approaches (DBSCAN, KMeans, Agglomerative) - Quality metrics for evaluating clustering results - Export capabilities for selections and generated features
Daoud, Hazem [Lawrence Berkeley National Laborator↗
SOX2-driven enhancer landscape defines the transcriptional architecture of retinogenesis
Retinal neurogenesis is mediated by the coordinated activities of a complex gene regulatory network (GRN) of transcription factors (TFs) in multipotent retinal progenitor cells (RPCs). How this GRN mechanistically guides neural competence remains poorly understood. In this study, we present integrated transcriptional, genetic and genomic analyses to uncover the regulatory mechanisms of SOX2, a key factor in establishing neural identity in RPCs. We show that SOX2 is preferentially enriched in the RPC-specific enhancer landscape associated with essential regulators of retinogenesis. Disruption of SOX2 expression impairs retinogenesis, marked by a selective loss of enhancer activity near genes essential for RPC proliferation and lineage specification. We identified the RPC transcription factor VSX2 as a binding partner for SOX2 and, together, SOX2 and VSX2 co-target a core, retina-specific chromatin repertoire characterized by enhanced TF binding and robust chromatin accessibility. This cooperative binding establishes a shared SOX2-VSX2 transcriptional code that promotes the expression of crucial regulators of neurogenesis while repressing the acquisition of alternative lineage cell fate. Our data illuminate fundamental biological insights on how transcription factors act in concert to drive chromatin-based genetic programs underlying retinal neural identity.
Chromatin↗
Ultrastructure of meristem and root cap of pea seedlings under spaceflight conditions
Data of electron microscopic analysis of meristem and root cap of pea seedlings grown aboard the Salyut-6 orbital research station in the Oazis apparatus and in the laboratory are presented. The main morphological and anatomical characteristics of the test and control plants are shown to be similar. At the same time, some differences are found in the structural and functional organization of the experimental cells as compared to the controls. They concern first of all the plastic apparatus, mitochondria and Golgi apparatus. It is assumed that cell function for certain periods of weightlessness on the whole ensures execution of the cytodifferentiation programs genetically determined on the Earth. Biochemical and physiological processes vary rather markedly due to lack of initially rigorous determination.
Sytnyk, K. M.↗
SoftLab: A Soft-Computing Software for Experimental Research with Commercialization Aspects
SoftLab is a software environment for research and development in intelligent modeling/control using soft-computing paradigms such as fuzzy logic, neural networks, genetic algorithms, and genetic programs. SoftLab addresses the inadequacies of the existing soft-computing software by supporting comprehensive multidisciplinary functionalities from management tools to engineering systems. Furthermore, the built-in features help the user process/analyze information more efficiently by a friendly yet powerful interface, and will allow the user to specify user-specific processing modules, hence adding to the standard configuration of the software environment.
Akbarzadeh-T, M.-R.↗
Automated Design of Quantum Circuits
In order to design a quantum circuit that performs a desired quantum computation, it is necessary to find a decomposition of the unitary matrix that represents that computation in terms of a sequence of quantum gate operations. To date, such designs have either been found by hand or by exhaustive enumeration of all possible circuit topologies. In this paper we propose an automated approach to quantum circuit design using search heuristics based on principles abstracted from evolutionary genetics, i.e. using a genetic programming algorithm adapted specially for this problem. We demonstrate the method on the task of discovering quantum circuit designs for quantum teleportation. We show that to find a given known circuit design (one which was hand-crafted by a human), the method considers roughly an order of magnitude fewer designs than naive enumeration. In addition, the method finds novel circuit designs superior to those previously known.
Williams, Colin P.↗
Evolution of Autonomous Self-Righting Behaviors for Articulated Nanorovers
Miniature rovers with articulated mobility mechanisms are being developed for planetary surface exploration on Mars and small solar system bodies. These vehicles are designed to be capable of autonomous recovery from overturning during surface operations. This paper describes a computational means of developing motion behaviors that achieve the autonomous recovery function. It proposes a control software design approach aimed at reducing the effort involved in developing self-righting behaviors. The approach is based on the integration of evolutionary computing with a dynamics simulation environment for evolving and evaluating motion behaviors. The automated behavior design approach is outlined and its underlying genetic programming infrastructure is described.
Tunstel, Edward↗
An amphioxus winged helix/forkhead gene, AmphiFoxD: insights into vertebrate neural crest evolution
During amphioxus development, the neural plate is bordered by cells expressing many genes with homologs involved in vertebrate neural crest induction. However, these amphioxus cells evidently lack additional genetic programs for the cell delaminations, migrations, and differentiations characterizing definitive vertebrate neural crest. We characterize an amphioxus winged helix/forkhead gene (AmphiFoxD) closely related to vertebrate FoxD genes. Phylogenetic analysis indicates that the AmphiFoxD is basal to vertebrate FoxD1, FoxD2, FoxD3, FoxD4, and FoxD5. One of these vertebrate genes (FoxD3) consistently marks neural crest during development. Early in amphioxus development, AmphiFoxD is expressed medially in the anterior neural plate as well as in axial (notochordal) and paraxial mesoderm; later, the gene is expressed in the somites, notochord, cerebral vesicle (diencephalon), and hindgut endoderm. However, there is never any expression in cells bordering the neural plate. We speculate that an AmphiFoxD homolog in the common ancestor of amphioxus and vertebrates was involved in histogenic processes in the mesoderm (evagination and delamination of the somites and notochord); then, in the early vertebrates, descendant paralogs of this gene began functioning in the presumptive neural crest bordering the neural plate to help make possible the delaminations and cell migrations that characterize definitive vertebrate neural crest. Copyright 2002 Wiley-Liss, Inc.
NASA Discipline Evolutionary Biology↗
AmphiFoxE4, an amphioxus winged helix/forkhead gene encoding a protein closely related to vertebrate thyroid transcription factor-2: expression during pharyngeal development
The full-length sequence and developmental expression of amphioxus AmphiFoxE4 are described. Transcripts of the gene are first detected in the pharyngeal endoderm, where the club-shaped gland is forming and subsequently in the definitive gland itself. AmphiFoxE4 is closely related to vertebrate genes encoding the thyroid-specific transcription factor-2 (TTF2), which plays an early developmental role in the morphogenesis of the thyroid gland and a later role in hormone-mediated control of thyroid function. In amphioxus, AmphiFoxE4 expression is not thyroid specific because the club-shaped gland, the only structure expressing the gene, is not homologous to the vertebrate thyroid; instead, the thyroid homologue of amphioxus is a specialized region of the pharyngeal endoderm called the endostyle. We propose that (a) the pharynx of an amphioxus-like ancestor of the vertebrates included a club-shaped gland that expressed FoxE4 as well as an endostyle that did not, and (b) the club-shaped gland soon disappeared in the vertebrate line of descent but (c) not before there was a homeogenetic transfer of FoxE4 expression from the club-shaped gland to the nearby endostyle. Such a transfer could have provided part of the genetic program enabling the endostyle to separate from the pharyngeal endoderm and migrate away as the rudiment of the thyroid gland.
Non-NASA Center↗
An Evolved Antenna for Deployment on NASA's Space Technology 5 Mission
We present an evolved X - band antenna design and flight prototype currently on schedule to be deployed on NASA's Space Technology 5(ST5) spacecraft. Current methods of designing and optimizing antennas by hand are time and labor intensive, limit complexity and require significant expertise and experience. Evolutionary design techniques can overcome these limitations by searching the design space and automatically finding effective solutions that would ordinarily not be found. The ST5 antenna was evolved to meet a challenging set of mission requirements, most notably the combination of wide beamwidth for a circularly-polarized wave and wide bandwidth.Two evolutionary algorithms were used: one used a genetic algorithm style representation that did not allow branching in the antenna arms: the second used a genetic programming style tree-structured representation that allowed branching in the antenna arms. The highest performance antennas from both algorithms were fabricated and tested and both yielded similar performance. Both were comparable in performance to a hand-designed antenna produced by a contractor for the mission, and so we consider them examples of human-competitive performance by evolutionary algorithms. One of the evolved antenna prototypes is undergoing flight qualification testing.
Lohn, Jason D.↗
Steady-State ALPS for Real-Valued Problems
The two objectives of this paper are to describe a steady-state version of the Age-Layered Population Structure (ALPS) Evolutionary Algorithm (EA) and to compare it against other GAs on real-valued problems. Motivation for this work comes from our previous success in demonstrating that a generational version of ALPS greatly improves search performance on a Genetic Programming problem. In making steady-state ALPS some modifications were made to the method for calculating age and the method for moving individuals up layers. To demonstrate that ALPS works well on real-valued problems we compare it against CMA-ES and Differential Evolution (DE) on five challenging, real-valued functions and on one real-world problem. While CMA-ES and DE outperform ALPS on the two unimodal test functions, ALPS is much better on the three multimodal test problems and on the real-world problem. Further examination shows that, unlike the other GAs, ALPS maintains a genotypically diverse population throughout the entire search process. These findings strongly suggest that the ALPS paradigm is better able to avoid premature convergence then the other GAs.
Hornby, Gregory S.↗