Evolving differential equations with developmental linear genetic programming and epigenetic hill climbing. - William G. La Cava; Lee Spector; Kourosh Danai; Matthew Lackner (GECCO (Companion), 2014)

Inheritable Epigenetics in Genetic Programming. - William G. La Cava; Lee Spector (GPTP, 2014)

Analyzing a Decade of Human-Competitive ("HUMIE") Winners: What Can We Learn? - Karthik Kannappan; Lee Spector; Moshe Sipper; Thomas Helmuth; William G. La Cava; Jake Wisdom; Omri Bernstein (GPTP, 2014)

Genetic Programming with Epigenetic Local Search. - William G. La Cava; Thomas Helmuth; Lee Spector; Kourosh Danai (GECCO, 2015)

Epsilon-Lexicase Selection for Regression. - William G. La Cava; Lee Spector; Kourosh Danai (GECCO, 2016)

Inference of compact nonlinear dynamic models by epigenetic local search. - William G. La Cava; Kourosh Danai; Lee Spector (Eng. Appl. Artif. Intell., 2016)

Gradient-based adaptation of continuous dynamic model structures. - William G. La Cava; Kourosh Danai (Int. J. Syst. Sci., 2016)

A General Feature Engineering Wrapper for Machine Learning Using epsilon -Lexicase Survival. - William G. La Cava; Jason H. Moore (EuroGP, 2017)

Genetic Programming Representations for Multi-dimensional Feature Learning in Biomedical Classification. - William G. La Cava; Sara Silva; Leonardo Vanneschi; Lee Spector; Jason H. Moore (EvoApplications (1), 2017)

Ensemble representation learning: an analysis of fitness and survival for wrapper-based genetic programming methods. - William G. La Cava; Jason H. Moore (GECCO, 2017)

A System for Accessible Artificial Intelligence. - Randal S. Olson; Moshe Sipper; William G. La Cava; Sharon Tartarone; Steven Vitale; Weixuan Fu; Patryk Orzechowski; Ryan J. Urbanowicz; John H. Holmes; Jason H. Moore (GPTP, 2017)

Relaxations of Lexicase Parent Selection. - Lee Spector; William G. La Cava; Saul Shanabrook; Thomas Helmuth; Edward R. Pantridge (GPTP, 2017)

PMLB: a large benchmark suite for machine learning evaluation and comparison. - Randal S. Olson; William G. La Cava; Patryk Orzechowski; Ryan J. Urbanowicz; Jason H. Moore (BioData Min., 2017)

A System for Accessible Artificial Intelligence. - Randal S. Olson; Moshe Sipper; William G. La Cava; Sharon Tartarone; Steven Vitale; Weixuan Fu; John H. Holmes; Jason H. Moore (CoRR, 2017)

$ε$-Lexicase selection: a probabilistic and multi-objective analysis of lexicase selection in continuous domains. - William G. La Cava; Thomas Helmuth; Lee Spector; Jason H. Moore (CoRR, 2017)

An analysis of ϵ-lexicase selection for large-scale many-objective optimization. - William G. La Cava; Jason H. Moore (GECCO (Companion), 2018)

A multidimensional genetic programming approach for identifying epsistatic gene interactions. - William G. La Cava; Sara Silva; Kourosh Danai; Lee Spector; Leonardo Vanneschi; Jason H. Moore (GECCO (Companion), 2018)

Where are we now?: a large benchmark study of recent symbolic regression methods. - Patryk Orzechowski; William G. La Cava; Jason H. Moore (GECCO, 2018)

Behavioral search drivers and the role of elitism in soft robotics. - William G. La Cava; Jason H. Moore (ALIFE, 2018)

Data-driven advice for applying machine learning to bioinformatics problems. - Randal S. Olson; William G. La Cava; Zairah Mustahsan; Akshay Varik; Jason H. Moore (PSB, 2018)

Stochastic optimization approaches to learning concise representations. - William G. La Cava; Tilak Raj Singh; James Taggart; Srinivas Suri; Jason H. Moore (CoRR, 2018)

Relief-based feature selection: Introduction and review. - Ryan J. Urbanowicz; Melissa Meeker; William G. La Cava; Randal S. Olson; Jason H. Moore (J. Biomed. Informatics, 2018)

Interpretation of machine learning predictions for patient outcomes in electronic health records. - William G. La Cava; Christopher R. Bauer; Jason H. Moore; Sarah A. Pendergrass (AMIA, 2019)

Semantic variation operators for multidimensional genetic programming. - William G. La Cava; Jason H. Moore (GECCO, 2019)

Learning concise representations for regression by evolving networks of trees. - William G. La Cava; Tilak Raj Singh; James Taggart; Srinivas Suri; Jason H. Moore (ICLR (Poster), 2019)

Evaluating recommender systems for AI-driven data science. - William G. La Cava; Heather Williams; Weixuan Fu; Jason H. Moore (CoRR, 2019)

A Probabilistic and Multi-Objective Analysis of Lexicase Selection and ε-Lexicase Selection. - William G. La Cava; Thomas Helmuth; Lee Spector; Jason H. Moore (Evol. Comput., 2019)

Multidimensional genetic programming for multiclass classification. - William G. La Cava; Sara Silva; Kourosh Danai; Lee Spector; Leonardo Vanneschi; Jason H. Moore (Swarm Evol. Comput., 2019)

Genetic programming approaches to learning fair classifiers. - William G. La Cava; Jason H. Moore (GECCO, 2020)

Theory Manual for the Tuned Mass Damper Module in FAST v8. - William G. La Cava; Matthew A. Lackner (CoRR, 2020)

Learning feature spaces for regression with genetic programming. - William G. La Cava; Jason H. Moore (Genet. Program. Evolvable Mach., 2020)

Lexicase Selection. - Thomas Helmuth; William G. La Cava (GECCO Companion, 2021)

Contemporary Symbolic Regression Methods and their Relative Performance. - William G. La Cava; Patryk Orzechowski; Bogdan Burlacu; Fabrício Olivetti de França; Marco Virgolin; Ying Jin; Michael Kommenda; Jason H. Moore (NeurIPS Datasets and Benchmarks, 2021)

Evaluating recommender systems for AI-driven biomedical informatics. - William G. La Cava; Heather Williams; Weixuan Fu; Steven Vitale; Durga Srivatsan; Jason H. Moore (Bioinform., 2021)

SLUG: Feature Selection Using Genetic Algorithms and Genetic Programming. - Nuno M. Rodrigues; João E. Batista; William G. La Cava; Leonardo Vanneschi; Sara Silva (EuroGP, 2022)

A comparative study of GP-based and state-of-the-art classifiers on a synthetic machine learning benchmark. - Patryk Orzechowski; Pawel Renc; William G. La Cava; Jason H. Moore; Arkadiusz Sitek; Jaroslaw Was; Joost B. Wagenaar (GECCO Companion, 2022)

Population Diversity Leads to Short Running Times of Lexicase Selection. - Thomas Helmuth; Johannes Lengler; William G. La Cava (PPSN (2), 2022)

PMLB v1.0: an open-source dataset collection for benchmarking machine learning methods. - Joseph D. Romano; Trang T. Le; William G. La Cava; John T. Gregg; Daniel J. Goldberg; Praneel Chakraborty; Natasha L. Ray; Daniel S. Himmelstein; Weixuan Fu; Jason H. Moore (Bioinform., 2022)

Proportional Multicalibration. - William G. La Cava; Elle Lett; Guangya Wan (CoRR, 2022)

Fair admission risk prediction with proportional multicalibration. - William G. La Cava; Elle Lett; Guangya Wan (CHIL, 2023)

Optimizing fairness tradeoffs in machine learning with multiobjective meta-models. - William G. La Cava (GECCO, 2023)

Interpretable Symbolic Regression for Data Science: Analysis of the 2022 Competition. - Fabrício Olivetti de França; Marco Virgolin; Michael Kommenda; Maimuna S. Majumder; Miles D. Cranmer; Guilherme Espada; Leon Ingelse; Alcides Fonseca; Mikel Landajuela; Brenden K. Petersen; Ruben Glatt; T. Nathan Mundhenk; et al. (CoRR, 2023)

A flexible symbolic regression method for constructing interpretable clinical prediction models. - William G. La Cava; Paul C. Lee; Imran Ajmal; Xiruo Ding; Priyanka Solanki; Jordana B. Cohen; Jason H. Moore; Daniel S. Herman (npj Digit. Medicine, 2023)

Informative missingness: What can we learn from patterns in missing laboratory data in the electronic health record? - Amelia L. M. Tan; Emily J. Getzen; Meghan R. Hutch; Zachary H. Strasser; Alba Gutiérrez-Sacristán; Trang T. Le; Arianna Dagliati; Michele Morris; David A. Hanauer; Bertrand Moal; Clara-Lea Bonzel; William Yuan; et al. (J. Biomed. Informatics, 2023)

Translating intersectionality to fair machine learning in health sciences. - Elle Lett; William G. La Cava (Nat. Mac. Intell., 2023)

Inexact Simplification of Symbolic Regression Expressions with Locality-sensitive Hashing. - Guilherme Seidyo Imai Aldeia; Fabrício Olivetti de França; William G. La Cava (GECCO, 2024)

Minimum variance threshold for epsilon-lexicase selection. - Guilherme Seidyo Imai Aldeia; Fabrício Olivetti de França; William G. La Cava (GECCO, 2024)

Cross-Care: Assessing the Healthcare Implications of Pre-training Data on Language Model Bias. - Shan Chen 004; Jack Gallifant; Mingye Gao; Pedro Moreira; Nikolaj Munch; Ajay Muthukkumar; Arvind Rajan; Jaya Kolluri; Amelia Fiske; Janna Hastings; Hugo J. W. L. Aerts; Brian Anthony; et al. (NeurIPS, 2024)

Benchmarking mortality risk prediction from electrocardiograms. - Platon Lukyanenko; Joshua Mayourian; Mingxuan Liu; John K. Triedman; Sunil J. Ghelani; William G. La Cava (CoRR, 2024)

Exploring SLUG: Feature Selection Using Genetic Algorithms and Genetic Programming. - Nuno M. Rodrigues; João E. Batista; William G. La Cava; Leonardo Vanneschi; Sara Silva (SN Comput. Sci., 2024)

Call for Action: towards the next generation of symbolic regression benchmark. - Guilherme Seidyo Imai Aldeia; Hengzhe Zhang; Geoffrey F. Bomarito; Miles D. Cranmer; Alcides Fonseca; Bogdan Burlacu; William G. La Cava; Fabrício Olivetti de França (CoRR, 2025)

A review of "Symbolic Regression" by Gabriel Kronberger, Bogdan Burlacu, Michael Kommenda, Stephan M. Winkler, and Michael Affenzeller, ISBN 978-1-138-05481-3, 2024, CRC Press. - William G. La Cava (Genet. Program. Evolvable Mach., 2025)

Iterative Learning of Computable Phenotypes for Treatment Resistant Hypertension using Large Language Models. - Guilherme Seidyo Imai Aldeia; Daniel S. Herman; William G. La Cava (CoRR, 2025)

Towards Integrated Alignment. - Ben Y. Reis; William G. La Cava (CoRR, 2025)

SRBench++: Principled Benchmarking of Symbolic Regression With Domain-Expert Interpretation. - Fabrício Olivetti de França; Marco Virgolin; Michael Kommenda; Maimuna S. Majumder; Miles D. Cranmer; Guilherme Espada; Leon Ingelse; Alcides Fonseca; Mikel Landajuela; Brenden K. Petersen; Ruben Glatt; T. Nathan Mundhenk; et al. (IEEE Trans. Evol. Comput., 2025)