{"about":{"non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","site":"https://codewithpapers.app","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page","syntology":{"site":"https://syntology.ai","developers":"https://syntology.ai/developers","mcp":{"server":"https://syntology.ai/mcp","transport":"streamable-http","server_card":"https://syntology.ai/.well-known/mcp/server-card.json","auth":{"type":"trial token, no account","trial_token":"https://syntology.ai/api/oauth/trial/token","method":"POST","docs":"https://syntology.ai/developers"}},"have":"https://syntology.ai/api/graph/have?x=<method, arXiv id or title> (free, answers coverage only)","paper_base":"https://syntology.ai/paper/","atlas_base":"https://app.syntology.ai/?focus="},"machine_readable":[{"url":"https://codewithpapers.app/llms.txt","what":"the machine catalog: every machine-readable file, counted"},{"url":"https://codewithpapers.app/index/manifest.json","what":"paper-to-code index by arXiv id, with Syntology's counts"},{"url":"https://codewithpapers.app/search/manifest.json","what":"site search index (titles, authors) and its files"},{"url":"https://codewithpapers.app/download","what":"bulk files: Syntology's layer, described there"},{"url":"https://codewithpapers.app/build_manifest.json","what":"the build record: inputs, counts, exclusions, probes"}]},"url":"/task/symbolic-regression/papers/4","list_of":"/task/symbolic-regression","task":"Symbolic Regression","archive":{"snapshot":"2025-07-28"},"key_notes":{"n_ran_checked":"legacy name, kept unchanged so existing readers do not break: it counts the samples that ran with no instrument failure (honoured, violated, and ran with no contract checked); it does not mean a contract was checked, and the pages print it as 'K with no instrument failure', not 'K checked'","n_constructed":"a sub-count of the samples that ran, never subtracted from them and never a failure: an executed sample whose run returned an instance of its own class (fixture_out_type equals the entry name): the run built an object and did not compute a result (Syntology's RAN record, counts.constructed)"},"syntology_read_at":"2026-09-28T10:30:06+00:00","order":"archive","order_definition":"repositories listed in the archive (most first), then date (newest first), then slug","page":4,"pages_in_order":5,"rows_per_page":100,"rows":[301,400],"of":418,"counts":{"archive_papers_tagged":418,"with_a_code_link":155,"where_syntology_ran_a_sample":33,"not_listed_spam_title":0,"listed":418,"listed_where_code_ran":33,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":29,"every_run_a_failure_of_syntologys_instrument":4,"listed_with_a_run_with_no_instrument_failure":29,"listed_every_run_a_failure_of_syntologys_instrument":4,"filter":{"states":["a run with no instrument failure","any run, instrument failures included"],"default":"a run with no instrument failure","note":"on the 'only where code ran' pages the default hides, in the browser, the rows where every run was a failure of Syntology's instrument; the second state shows them again. Rows are hidden, never re-ordered; these twins list every row"}},"definition":"distinct papers the archive tags; 'where Syntology ran a sample' counts papers with at least one harvested sample that ran, which is not a correctness claim"},"first_page":"/task/symbolic-regression","prev":"/task/symbolic-regression/papers/3","next":"/task/symbolic-regression/papers/5","papers":[{"url":null,"slug":"down-sampled-epsilon-lexicase-selection-for","title":"Down-Sampled Epsilon-Lexicase Selection for Real-World Symbolic Regression Problems","date":"2023-02-08","arxiv_id":"2302.04301","repositories_listed":0,"syntology":null},{"url":null,"slug":"genetic-programming-based-symbolic-regression","title":"Genetic Programming Based Symbolic Regression for Analytical Solutions to Differential Equations","date":"2023-02-07","arxiv_id":"2302.03175","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-symbolic-models-for-graph-structured","title":"Learning Symbolic Models for Graph-structured Physical Mechanism","date":"2023-02-02","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"neural-networks-for-symbolic-regression","title":"Toward Physically Plausible Data-Driven Models: A Novel Neural Network Approach to Symbolic Regression","date":"2023-02-01","arxiv_id":"2302.00773","repositories_listed":0,"syntology":null},{"url":null,"slug":"incorporating-background-knowledge-in","title":"Incorporating Background Knowledge in Symbolic Regression using a Computer Algebra System","date":"2023-01-27","arxiv_id":"2301.11919","repositories_listed":0,"syntology":null},{"url":null,"slug":"symbolic-expression-generation-via","title":"Symbolic expression generation via Variational Auto-Encoder","date":"2023-01-15","arxiv_id":"2301.06064","repositories_listed":0,"syntology":null},{"url":null,"slug":"discovery-of-structure-property-relations-for","title":"Discovery of structure-property relations for molecules via hypothesis-driven active learning over the chemical space","date":"2023-01-06","arxiv_id":"2301.02665","repositories_listed":0,"syntology":null},{"url":null,"slug":"steel-phase-kinetics-modeling-using-symbolic","title":"Steel Phase Kinetics Modeling using Symbolic Regression","date":"2022-12-19","arxiv_id":"2212.10284","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-pinn-approach-to-symbolic-differential","title":"A PINN Approach to Symbolic Differential Operator Discovery with Sparse Data","date":"2022-12-09","arxiv_id":"2212.04630","repositories_listed":0,"syntology":null},{"url":null,"slug":"p-expression-grammar-probability-of-deriving","title":"P(Expression|Grammar): Probability of deriving an algebraic expression with a probabilistic context-free grammar","date":"2022-12-01","arxiv_id":"2212.00751","repositories_listed":0,"syntology":null},{"url":null,"slug":"is-the-machine-smarter-than-the-theorist","title":"Is the Machine Smarter than the Theorist: Deriving Formulas for Particle Kinematics with Symbolic Regression","date":"2022-11-15","arxiv_id":"2211.08420","repositories_listed":0,"syntology":null},{"url":null,"slug":"generalizability-of-functional-forms-for","title":"Generalizability of Functional Forms for Interatomic Potential Models Discovered by Symbolic Regression","date":"2022-10-27","arxiv_id":"2210.15124","repositories_listed":0,"syntology":null},{"url":null,"slug":"shape-constrained-symbolic-regression-with","title":"Shape-constrained Symbolic Regression with NSGA-III","date":"2022-09-28","arxiv_id":"2209.13851","repositories_listed":0,"syntology":null},{"url":null,"slug":"symbolic-regression-with-fast-function","title":"Symbolic Regression with Fast Function Extraction and Nonlinear Least Squares Optimization","date":"2022-09-20","arxiv_id":"2209.09675","repositories_listed":0,"syntology":null},{"url":null,"slug":"prediction-intervals-and-confidence-regions","title":"Prediction Intervals and Confidence Regions for Symbolic Regression Models based on Likelihood Profiles","date":"2022-09-14","arxiv_id":"2209.06454","repositories_listed":0,"syntology":null},{"url":null,"slug":"symplectically-integrated-symbolic-regression","title":"Symplectically Integrated Symbolic Regression of Hamiltonian Dynamical Systems","date":"2022-09-04","arxiv_id":"2209.01521","repositories_listed":0,"syntology":null},{"url":null,"slug":"local-optimization-often-is-ill-conditioned","title":"Local Optimization Often is Ill-conditioned in Genetic Programming for Symbolic Regression","date":"2022-09-02","arxiv_id":"2209.00942","repositories_listed":0,"syntology":null},{"url":null,"slug":"lexicase-selection-at-scale","title":"Lexicase Selection at Scale","date":"2022-08-23","arxiv_id":"2208.10719","repositories_listed":0,"syntology":null},{"url":null,"slug":"interpretable-polynomial-neural-ordinary","title":"Interpretable Polynomial Neural Ordinary Differential Equations","date":"2022-08-09","arxiv_id":"2208.05072","repositories_listed":0,"syntology":null},{"url":null,"slug":"what-can-we-learn-by-predicting-accuracy","title":"What can we Learn by Predicting Accuracy?","date":"2022-08-02","arxiv_id":"2208.01358","repositories_listed":0,"syntology":null},{"url":null,"slug":"symbolic-regression-is-np-hard","title":"Symbolic Regression is NP-hard","date":"2022-07-03","arxiv_id":"2207.01018","repositories_listed":0,"syntology":null},{"url":null,"slug":"understanding-physical-effects-for-effective","title":"Understanding Physical Effects for Effective Tool-use","date":"2022-06-30","arxiv_id":"2206.14998","repositories_listed":0,"syntology":null},{"url":null,"slug":"binary-and-multinomial-classification-through","title":"Binary and Multinomial Classification through Evolutionary Symbolic Regression","date":"2022-06-25","arxiv_id":"2206.12706","repositories_listed":0,"syntology":null},{"url":null,"slug":"symbolic-regression-boosting","title":"Symbolic-Regression Boosting","date":"2022-06-24","arxiv_id":"2206.12082","repositories_listed":0,"syntology":null},{"url":null,"slug":"symbolic-regression-for-space-applications","title":"Symbolic Regression for Space Applications: Differentiable Cartesian Genetic Programming Powered by Multi-objective Memetic Algorithms","date":"2022-06-13","arxiv_id":"2206.06213","repositories_listed":0,"syntology":null},{"url":null,"slug":"correlation-versus-rmse-loss-functions-in","title":"Correlation versus RMSE Loss Functions in Symbolic Regression Tasks","date":"2022-05-31","arxiv_id":"2205.15990","repositories_listed":0,"syntology":null},{"url":null,"slug":"gsr-a-generalized-symbolic-regression","title":"GSR: A Generalized Symbolic Regression Approach","date":"2022-05-31","arxiv_id":"2205.15569","repositories_listed":0,"syntology":null},{"url":null,"slug":"symbolic-expression-transformer-a-computer","title":"Symbolic Expression Transformer: A Computer Vision Approach for Symbolic Regression","date":"2022-05-24","arxiv_id":"2205.11798","repositories_listed":0,"syntology":null},{"url":null,"slug":"taylor-genetic-programming-for-symbolic","title":"Taylor Genetic Programming for Symbolic Regression","date":"2022-04-28","arxiv_id":"2205.09751","repositories_listed":0,"syntology":null},{"url":null,"slug":"coefficient-mutation-in-the-gene-pool-optimal","title":"Coefficient Mutation in the Gene-pool Optimal Mixing Evolutionary Algorithm for Symbolic Regression","date":"2022-04-26","arxiv_id":"2204.12159","repositories_listed":0,"syntology":null},{"url":null,"slug":"exploring-hidden-semantics-in-neural-networks","title":"Exploring Hidden Semantics in Neural Networks with Symbolic Regression","date":"2022-04-22","arxiv_id":"2204.10529","repositories_listed":0,"syntology":null},{"url":null,"slug":"automated-learning-of-interpretable-models","title":"Automated Learning of Interpretable Models with Quantified Uncertainty","date":"2022-04-12","arxiv_id":"2205.01626","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-low-cost-robot-science-kit-for-education","title":"A Low-Cost Robot Science Kit for Education with Symbolic Regression for Hypothesis Discovery and Validation","date":"2022-04-08","arxiv_id":"2204.04187","repositories_listed":0,"syntology":null},{"url":null,"slug":"failed-disruption-propagation-in-integer","title":"Failed Disruption Propagation in Integer Genetic Programming","date":"2022-04-04","arxiv_id":"2204.13997","repositories_listed":0,"syntology":null},{"url":null,"slug":"explainable-artificial-intelligence-for-5","title":"Explainable Artificial Intelligence for Exhaust Gas Temperature of Turbofan Engines","date":"2022-03-24","arxiv_id":"2203.13108","repositories_listed":0,"syntology":null},{"url":null,"slug":"neural-network-directed-genetic-programmer","title":"Neural-Network-Directed Genetic Programmer for Discovery of Governing Equations","date":"2022-03-15","arxiv_id":"2203.08808","repositories_listed":0,"syntology":null},{"url":null,"slug":"evolvability-degeneration-in-multi-objective","title":"Evolvability Degeneration in Multi-Objective Genetic Programming for Symbolic Regression","date":"2022-02-14","arxiv_id":"2202.06983","repositories_listed":0,"syntology":null},{"url":null,"slug":"active-learning-improves-performance-on","title":"Active Learning Improves Performance on Symbolic RegressionTasks in StackGP","date":"2022-02-09","arxiv_id":"2202.04708","repositories_listed":0,"syntology":null},{"url":null,"slug":"rediscovering-orbital-mechanics-with-machine","title":"Rediscovering orbital mechanics with machine learning","date":"2022-02-04","arxiv_id":"2202.02306","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-symbolic-regression-for-recurrent","title":"Deep Symbolic Regression for Recurrent Sequences","date":"2022-01-12","arxiv_id":"2201.04600","repositories_listed":0,"syntology":null},{"url":null,"slug":"analytical-modelling-of-exoplanet-transit","title":"Analytical Modelling of Exoplanet Transit Specroscopy with Dimensional Analysis and Symbolic Regression","date":"2021-12-22","arxiv_id":"2112.11600","repositories_listed":0,"syntology":null},{"url":null,"slug":"accelerating-understanding-of-scientific","title":"Accelerating Understanding of Scientific Experiments with End to End Symbolic Regression","date":"2021-12-07","arxiv_id":"2112.04023","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-bayesian-symbolic-approach-to-reasoning-and","title":"A Bayesian-Symbolic Approach to Reasoning and Learning in Intuitive Physics","date":"2021-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"building-the-building-blocks-from","title":"Building the Building Blocks: From Simplification to Winning Trees in Genetic Programming","date":"2021-09-29","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"d-code-discovering-closed-form-odes-from","title":"D-CODE: Discovering Closed-form ODEs from Observed Trajectories","date":"2021-09-29","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"interpreting-black-boxes-using-primitive","title":"Interpreting Black-boxes Using Primitive Parameterized Functions","date":"2021-09-29","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"cluster-analysis-of-a-symbolic-regression","title":"Cluster Analysis of a Symbolic Regression Search Space","date":"2021-09-28","arxiv_id":"2109.13898","repositories_listed":0,"syntology":null},{"url":null,"slug":"symbolic-regression-by-exhaustive-search","title":"Symbolic Regression by Exhaustive Search: Reducing the Search Space Using Syntactical Constraints and Efficient Semantic Structure Deduplication","date":"2021-09-28","arxiv_id":"2109.13895","repositories_listed":0,"syntology":null},{"url":null,"slug":"complexity-measures-for-multi-objective","title":"Complexity Measures for Multi-objective Symbolic Regression","date":"2021-09-01","arxiv_id":"2109.00238","repositories_listed":0,"syntology":null},{"url":null,"slug":"data-aggregation-for-reducing-training-data","title":"Data Aggregation for Reducing Training Data in Symbolic Regression","date":"2021-08-24","arxiv_id":"2108.10660","repositories_listed":0,"syntology":null},{"url":null,"slug":"on-the-effectiveness-of-genetic-operations-in","title":"On the Effectiveness of Genetic Operations in Symbolic Regression","date":"2021-08-24","arxiv_id":"2108.10661","repositories_listed":0,"syntology":null},{"url":null,"slug":"smooth-symbolic-regression-transformation-of","title":"Smooth Symbolic Regression: Transformation of Symbolic Regression into a Real-valued Optimization Problem","date":"2021-08-06","arxiv_id":"2108.03274","repositories_listed":0,"syntology":null},{"url":null,"slug":"hash-based-tree-similarity-and-simplification","title":"Hash-Based Tree Similarity and Simplification in Genetic Programming for Symbolic Regression","date":"2021-07-22","arxiv_id":"2107.10640","repositories_listed":0,"syntology":null},{"url":null,"slug":"using-shape-constraints-for-improving","title":"Using Shape Constraints for Improving Symbolic Regression Models","date":"2021-07-20","arxiv_id":"2107.09458","repositories_listed":0,"syntology":null},{"url":null,"slug":"incorporating-domain-knowledge-into-neural","title":"Incorporating domain knowledge into neural-guided search","date":"2021-07-19","arxiv_id":"2107.09182","repositories_listed":0,"syntology":null},{"url":null,"slug":"predicting-friction-system-performance-with","title":"Predicting Friction System Performance with Symbolic Regression and Genetic Programming with Factor Variables","date":"2021-07-19","arxiv_id":"2107.09484","repositories_listed":0,"syntology":null},{"url":null,"slug":"identification-of-dynamical-systems-using","title":"Identification of Dynamical Systems using Symbolic Regression","date":"2021-07-06","arxiv_id":"2107.06131","repositories_listed":0,"syntology":null},{"url":null,"slug":"inferring-the-structure-of-ordinary","title":"Inferring the Structure of Ordinary Differential Equations","date":"2021-07-05","arxiv_id":"2107.07345","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-interpretable-prediction-model-for","title":"A data-based comparative review and AI-driven symbolic model for longitudinal dispersion coefficient in natural streams","date":"2021-06-17","arxiv_id":"2106.11026","repositories_listed":0,"syntology":null},{"url":null,"slug":"incorporating-domain-knowledge-into-neural-1","title":"Incorporating domain knowledge into neural-guided search via in situ priors and constraints","date":"2021-05-20","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"physical-constraint-embedded-neural-networks","title":"Physical Constraint Embedded Neural Networks for inference and noise regulation","date":"2021-05-19","arxiv_id":"2105.09146","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-symbolic-regression-method-for-dynamic","title":"A Symbolic Regression Method for Dynamic Modeling and Control of Quadrotor UAVs","date":"2021-05-07","arxiv_id":"2105.03032","repositories_listed":0,"syntology":null},{"url":null,"slug":"distilling-wikipedia-mathematical-knowledge","title":"Distilling Wikipedia mathematical knowledge into neural network models","date":"2021-04-13","arxiv_id":"2104.05930","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-approach-to-symbolic-regression-using-feyn","title":"An Approach to Symbolic Regression Using Feyn","date":"2021-04-12","arxiv_id":"2104.05417","repositories_listed":0,"syntology":null},{"url":null,"slug":"shape-constrained-symbolic-regression","title":"Shape-constrained Symbolic Regression -- Improving Extrapolation with Prior Knowledge","date":"2021-03-29","arxiv_id":"2103.15624","repositories_listed":0,"syntology":null},{"url":null,"slug":"zoetrope-genetic-programming-for-regression","title":"Zoetrope Genetic Programming for Regression","date":"2021-02-26","arxiv_id":"2102.13388","repositories_listed":0,"syntology":null},{"url":null,"slug":"data-driven-formulation-of-natural-laws-by","title":"Data-driven formulation of natural laws by recursive-LASSO-based symbolic regression","date":"2021-02-18","arxiv_id":"2102.09210","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-symbolic-expressions-mixed-integer","title":"Learning Symbolic Expressions: Mixed-Integer Formulations, Cuts, and Heuristics","date":"2021-02-16","arxiv_id":"2102.08351","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-bayesian-symbolic-approach-to-learning-and","title":"A Bayesian-Symbolic Approach to Learning and Reasoning for Intuitive Physics","date":"2021-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-symbolic-expressions-via-gumbel-max","title":"Learning Symbolic Expressions via Gumbel-Max Equation Learner Networks","date":"2020-12-12","arxiv_id":"2012.06921","repositories_listed":0,"syntology":null},{"url":null,"slug":"logic-guided-genetic-algorithms","title":"Logic Guided Genetic Algorithms","date":"2020-10-21","arxiv_id":"2010.11328","repositories_listed":0,"syntology":null},{"url":null,"slug":"symbolic-regression-using-mixed-integer","title":"Symbolic Regression using Mixed-Integer Nonlinear Optimization","date":"2020-06-11","arxiv_id":"2006.06813","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-functions-to-study-the-benefit-of","title":"Learning Functions to Study the Benefit of Multitask Learning","date":"2020-06-09","arxiv_id":"2006.05561","repositories_listed":0,"syntology":null},{"url":null,"slug":"symbolic-pregression-discovering-physical","title":"Symbolic Pregression: Discovering Physical Laws from Distorted Video","date":"2020-05-19","arxiv_id":"2005.11212","repositories_listed":0,"syntology":null},{"url":null,"slug":"fitness-landscape-analysis-of-dimensionally","title":"Fitness Landscape Analysis of Dimensionally-Aware Genetic Programming Featuring Feynman Equations","date":"2020-04-27","arxiv_id":"2004.12762","repositories_listed":0,"syntology":null},{"url":null,"slug":"mate-a-model-based-algorithm-tuning-engine","title":"MATE: A Model-based Algorithm Tuning Engine","date":"2020-04-27","arxiv_id":"2004.12750","repositories_listed":0,"syntology":null},{"url":null,"slug":"swarm-programming-using-moth-flame","title":"Swarm Programming Using Moth-Flame Optimization and Whale Optimization Algorithms","date":"2020-04-25","arxiv_id":"2005.04151","repositories_listed":0,"syntology":null},{"url":null,"slug":"symbolic-regression-driven-by-training-data","title":"Symbolic Regression Driven by Training Data and Prior Knowledge","date":"2020-04-24","arxiv_id":"2004.11947","repositories_listed":0,"syntology":null},{"url":null,"slug":"traffic-modelling-and-prediction-via-symbolic","title":"Traffic Modelling and Prediction via Symbolic Regression on Road Sensor Data","date":"2020-02-14","arxiv_id":"2002.06095","repositories_listed":0,"syntology":null},{"url":null,"slug":"explainable-machine-learning-control-robust","title":"Explainable Machine Learning Control -- robust control and stability analysis","date":"2020-01-23","arxiv_id":"2001.10056","repositories_listed":0,"syntology":null},{"url":null,"slug":"analytic-continued-fractions-for-regression-a","title":"Analytic Continued Fractions for Regression: A Memetic Algorithm Approach","date":"2019-12-18","arxiv_id":"2001.00624","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-symbolic-regression","title":"Deep symbolic regression","date":"2019-09-25","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-symbolic-physics-with-graph-networks","title":"Learning Symbolic Physics with Graph Networks","date":"2019-09-12","arxiv_id":"1909.05862","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-search-for-the-underlying-equation","title":"A Search for the Underlying Equation Governing Similar Systems","date":"2019-08-27","arxiv_id":"1908.10673","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-new-deterministic-technique-for-symbolic","title":"A New Deterministic Technique for Symbolic Regression","date":"2019-08-16","arxiv_id":"1908.06754","repositories_listed":0,"syntology":null},{"url":null,"slug":"automatic-discovery-of-families-of-network","title":"Automatic Discovery of Families of Network Generative Processes","date":"2019-06-26","arxiv_id":"1906.12332","repositories_listed":0,"syntology":null},{"url":null,"slug":"exploration-and-exploitation-in-symbolic","title":"Exploration and Exploitation in Symbolic Regression using Quality-Diversity and Evolutionary Strategies Algorithms","date":"2019-06-10","arxiv_id":"1906.03959","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-novel-continuous-representation-of-genetic","title":"A Novel Neural Network-Based Symbolic Regression Method: Neuro-Encoded Expression Programming","date":"2019-04-06","arxiv_id":"1904.03368","repositories_listed":0,"syntology":null},{"url":null,"slug":"symbolic-regression-methods-for-reinforcement","title":"Symbolic Regression Methods for Reinforcement Learning","date":"2019-03-22","arxiv_id":"1903.09688","repositories_listed":0,"syntology":null},{"url":null,"slug":"data-driven-pde-discovery-with-evolutionary","title":"Data-driven PDE discovery with evolutionary approach","date":"2019-03-19","arxiv_id":"1903.08011","repositories_listed":0,"syntology":null},{"url":null,"slug":"online-diversity-control-in-symbolic","title":"Online Diversity Control in Symbolic Regression via a Fast Hash-based Tree Similarity Measure","date":"2019-02-03","arxiv_id":"1902.00882","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-libraries-of-subroutines-for","title":"Learning Libraries of Subroutines for Neurally–Guided Bayesian Program Induction","date":"2018-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"library-learning-for-neurally-guided-bayesian","title":"Library Learning for Neurally-Guided Bayesian Program Induction","date":"2018-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"symbolic-regression-based-genetic","title":"Symbolic regression based genetic approximations of the Colebrook equation for flow friction","date":"2018-08-29","arxiv_id":"1808.10394","repositories_listed":0,"syntology":null},{"url":null,"slug":"linear-combination-of-distance-measures-for","title":"Linear Combination of Distance Measures for Surrogate Models in Genetic Programming","date":"2018-07-03","arxiv_id":"1807.01019","repositories_listed":0,"syntology":null},{"url":null,"slug":"gp-rvm-genetic-programing-based-symbolic","title":"GP-RVM: Genetic Programing-based Symbolic Regression Using Relevance Vector Machine","date":"2018-06-07","arxiv_id":"1806.02502","repositories_listed":0,"syntology":null},{"url":null,"slug":"analysing-symbolic-regression-benchmarks","title":"Analysing Symbolic Regression Benchmarks under a Meta-Learning Approach","date":"2018-05-25","arxiv_id":"1805.10365","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-advanced-phenotypic-mutations-in","title":"Towards Advanced Phenotypic Mutations in Cartesian Genetic Programming","date":"2018-03-16","arxiv_id":"1803.06127","repositories_listed":0,"syntology":null},{"url":null,"slug":"on-the-generalizability-of-linear-and-non","title":"On the Generalizability of Linear and Non-Linear Region of Interest-Based Multivariate Regression Models for fMRI Data","date":"2018-02-03","arxiv_id":"1802.02423","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-greedy-search-tree-heuristic-for-symbolic","title":"A Greedy Search Tree Heuristic for Symbolic Regression","date":"2018-01-04","arxiv_id":"1801.01807","repositories_listed":0,"syntology":null}],"record_sha256":"ebd95959d3b14d997f7f0e3f5a93e4a5c3dc113bf1b8a04b7080fc85f041148c","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}