Rodrigues, N. M., Batista, J. E., La Cava, W., Vanneschi, L., & Silva, S. (2020). SLUG: Feature Selection Using Genetic Algorithms and Genetic Programming. Lecture Notes in Computer Science Springer. To Appear.

La Cava, W., Orzechowski, P., Burlacu, B., França, F. O. de, Virgolin, M., Jin, Y., Kommenda, M., & Moore, J. H. (2021). Contemporary Symbolic Regression Methods and their Relative Performance. Neurips Track on Datasets and Benchmarks. PMLR, arXiv, repo

La Cava, W., Lee, P.C., Ajmal, I., Ding, X., Cohen, J.B., Solanki, P., Moore, J.H., and Herman, D.S (2021). Application of concise machine learning to construct accurate and interpretable EHR computable phenotypes. In Review. medRxiv,

La Cava, W. & Moore, Jason H. (2020). Genetic programming approaches to learning fair classifiers. GECCO 2020. Best Paper Award. ACM, arXiv, software, experiments

La Cava, W., Williams, H., Fu, W., Vitale, S., Srivatsan, D., Moore, J. H. (2020). Evaluating recommender systems for AI-driven biomedical informatics. Bioinformatics. Oxford Press (open access), software, experiments

Bartz-Beielstein, T., Doerr, C., Berg, D. van den, Bossek, J., Chandrasekaran, S., Eftimov, T., Fischbach, A., Kerschke, P., La Cava, W., Lopez-Ibanez, M., Malan, K. M., Moore, J. H., Naujoks, B., Orzechowski, P., Volz, V., Wagner, M., & Weise, T. (2020). Benchmarking in Optimization: Best Practice and Open Issues. arXiv

Le, T. T., La Cava, W., Romano, J. D., Gregg, J. T., Goldberg, D. J., Chakraborty, P., Ray, N. L., Himmelstein, D., Fu, W., & Moore, J. H. (2020). PMLB v1.0: An open source dataset collection for benchmarking machine learning methods. arXiv, software

La Cava, W. & Moore, J.H. (2020). Learning feature spaces for regression with genetic programming. Genetic Programming and Evolvable Machines (GPEM). Springer, PDF, software, experiments

La Cava, W., Bauer, C. R., Moore, J. H., & Pendergrass, S. A. (2019). Interpretation of machine learning predictions for patient outcomes in electronic health records. AMIA 2019 Annual Symposium. arXiv, experiments

La Cava, W., & Moore, J. H. (2019). Semantic variation operators for multidimensional genetic programming. GECCO 2019. ACM, arXiv, software, experiments

La Cava, W., & Moore, J. H. (2019). Learning concise representations for regression by evolving networks of trees. ICLR 2019. arXiv, software, experiments

Wojcieszynski Jr, A. P., La Cava, W., Baumann, B. C., Lukens, J. N., Fotouhi Ghiam, A., Urbanowicz, R. J., … Metz, J. M. (2019). Machine Learning to Predict Toxicity in Head and Neck Cancer Patients Treated with Definitive Chemoradiation. International Journal of Radiation Oncology • Biology • Physics. ASTRO. PDF

La Cava, W., Silva, S., Danai, K., Spector, L., Vanneschi, L., & Moore, J. H. (2019). Multidimensional genetic programming for multiclass classification. Swarm and Evolutionary Computation. ScienceDirect, PDF, software

La Cava, W., Helmuth, T., Spector, L., & Moore, J. H. (2018). A probabilistic and multi-objective analysis of lexicase selection and epsilon-lexicase selection. Evolutionary Computation, 1–28. MIT Press, arXiv, experiments, software

La Cava, W., & Moore, J. H. (2018). An Analysis of epsilon-lexicase Selection for Large-scale Many-objective Optimization. GECCO 2018 Companion. ACM, experiments

La Cava, W., & Moore, J. H. (2018). Behavioral search drivers and the role of elitism in soft robotics. Artificial Life, 206–213. MIT Press (open access), repo

Orzechowski, P., La Cava, W., & Moore, J. H. (2018). Where are we now? A large benchmark study of recent symbolic regression methods. GECCO 2018. ACM, arXiv, repo

Urbanowicz, R. J., Meeker, M., La Cava, W., Olson, R. S., & Moore, J. H. (2018). Relief-based feature selection: Introduction and review. Journal of Biomedical Informatics, 85, 189 – 203. ScienceDirect, arXiv

La Cava, W., & Moore, J. H. (2017). A general feature engineering wrapper for machine learning using ϵ-lexicase survival. European Conference on Genetic Programming, 80–95. Springer, PDF, software

La Cava, W., & Moore, J. H. (2017). Ensemble representation learning: An analysis of fitness and survival for wrapper-based genetic programming methods. GECCO 2017. ACM, arXiv, software

La Cava, W., Sahare, K., & Danai, K. (2017). Restructuring Controllers to Accommodate Plant Nonlinearities. Journal of Dynamic Systems, Measurement, and Control, 139(8), 081004–081004–10. https://doi.org/10.1115/1.4035870. PDF

La Cava, W., Silva, S., Vanneschi, L., Spector, L., & Moore, J. (2017). Genetic Programming Representations for Multi-dimensional Feature Learning in Biomedical Classification. EvoStar Applications of Evolutionary Computation, 10199, 158–173. Springer, PDF

Olson, R. S., La Cava, W., Orzechowski, P., Urbanowicz, R. J., & Moore, J. H. (2017). PMLB: A Large Benchmark Suite for Machine Learning Evaluation and Comparison. BioData Mining. BMC (open access), arXiv, software

Olson, R. S., La Cava, William, Mustahsan, Z., Varik, A., & Moore, J. H. (2017). Data-driven Advice for Applying Machine Learning to Bioinformatics Problems. Pacific Symposium on Biocomputing (PSB). arXiv, experiments

La Cava, W., Danai, K., & Spector, L. (2016). Inference of compact nonlinear dynamic models by epigenetic local search. Engineering Applications of Artificial Intelligence, 55, 292–306. https://doi.org/10.1016/j.engappai.2016.07.004. PDF

La Cava, W., Spector, L., & Danai, K. (2016). Epsilon-Lexicase Selection for Regression. GECCO 2016. ACM, arXiv