145 citations · 793 across the 36 of their papers we have counts for
42 papers
Genetic Programming Theory and Practice: A Fifteen-Year Trajectory
Moshe Sipper, Jason H. Moore
The GPTP workshop series, which began in 2003, has served over the years as a focal meeting for genetic programming (GP) researchers. As such, we think it provides an excellent sou…
Coevolving Artistic Images Using OMNIREP
Moshe Sipper, Jason H. Moore, Ryan J. Urbanowicz
We have recently developed OMNIREP, a coevolutionary algorithm to discover both a representation and an interpreter that solve a particular problem of interest. Herein, we demonstr…
New Pathways in Coevolutionary Computation
Moshe Sipper, Jason H. Moore, Ryan J. Urbanowicz
The simultaneous evolution of two or more species with coupled fitness -- coevolution -- has been put to good use in the field of evolutionary computation. Herein, we present two n…
Automatically Balancing Model Accuracy and Complexity using Solution and Fitness Evolution (SAFE)
Moshe Sipper, Jason H. Moore, Ryan J. Urbanowicz
When seeking a predictive model in biomedical data, one often has more than a single objective in mind, e.g., attaining both high accuracy and low complexity (to promote interpreta…
Solution and Fitness Evolution (SAFE): A Study of Multiobjective Problems
Moshe Sipper, Jason H. Moore, Ryan J. Urbanowicz
We have recently presented SAFE -- Solution And Fitness Evolution -- a commensalistic coevolutionary algorithm that maintains two coevolving populations: a population of candidate…
Solution and Fitness Evolution (SAFE): Coevolving Solutions and Their Objective Functions
Moshe Sipper, Jason H. Moore, Ryan J. Urbanowicz
We recently highlighted a fundamental problem recognized to confound algorithmic optimization, namely, \textit{conflating} the objective with the objective function. Even when the…
Symbolic-Regression Boosting
Moshe Sipper, Jason H Moore
Modifying standard gradient boosting by replacing the embedded weak learner in favor of a strong(er) one, we present SyRBo: Symbolic-Regression Boosting. Experiments over 98 regres…
Contemporary Symbolic Regression Methods and their Relative Performance
William La Cava, Patryk Orzechowski, Bogdan Burlacu +5
Many promising approaches to symbolic regression have been presented in recent years, yet progress in the field continues to suffer from a lack of uniform, robust, and transparent…
Generative and reproducible benchmarks for comprehensive evaluation of machine learning classifiers
Patryk Orzechowski, Jason H. Moore
Understanding the strengths and weaknesses of machine learning (ML) algorithms is crucial for determine their scope of application. Here, we introduce the DIverse and GENerative ML…
EBIC.JL -- an Efficient Implementation of Evolutionary Biclustering Algorithm in Julia
Paweł Renc, Patryk Orzechowski, Aleksander Byrski +2
Biclustering is a data mining technique which searches for local patterns in numeric tabular data with main application in bioinformatics. This technique has shown promise in multi…
PMLB v1.0: An open source dataset collection for benchmarking machine learning methods
Joseph D. Romano, Trang T. Le, William La Cava +7
Motivation: Novel machine learning and statistical modeling studies rely on standardized comparisons to existing methods using well-studied benchmark datasets. Few tools exist that…
A Rigorous Machine Learning Analysis Pipeline for Biomedical Binary Classification: Application in Pancreatic Cancer Nested Case-control Studies with Implications for Bias Assessments
Ryan J. Urbanowicz, Pranshu Suri, Yuhan Cui +4
Machine learning (ML) offers a collection of powerful approaches for detecting and modeling associations, often applied to data having a large number of features and/or complex ass…
Benchmarking in Optimization: Best Practice and Open Issues
Thomas Bartz-Beielstein, Carola Doerr, Daan van den Berg +14
This survey compiles ideas and recommendations from more than a dozen researchers with different backgrounds and from different institutes around the world. Promoting best practice…
Is deep learning necessary for simple classification tasks?
Joseph D. Romano, Trang T. Le, Weixuan Fu +1
Automated machine learning (AutoML) and deep learning (DL) are two cutting-edge paradigms used to solve a myriad of inductive learning tasks. In spite of their successes, little gu…
Genetic programming approaches to learning fair classifiers
William La Cava, Jason H. Moore
Society has come to rely on algorithms like classifiers for important decision making, giving rise to the need for ethical guarantees such as fairness. Fairness is typically define…
SGP-DT: Semantic Genetic Programming Based on Dynamic Targets
Stefano Ruberto, Valerio Terragni, Jason H. Moore
Semantic GP is a promising approach that introduces semantic awareness during genetic evolution. This paper presents a new Semantic GP approach based on Dynamic Target (SGP-DT) tha…
Evaluating recommender systems for AI-driven biomedical informatics
William La Cava, Heather Williams, Weixuan Fu +3
Motivation: Many researchers with domain expertise are unable to easily apply machine learning to their bioinformatics data due to a lack of machine learning and/or coding expertis…
Semantic variation operators for multidimensional genetic programming
William La Cava, Jason H. Moore
Multidimensional genetic programming represents candidate solutions as sets of programs, and thereby provides an interesting framework for exploiting building block identification.…
Interpretation of machine learning predictions for patient outcomes in electronic health records
William La Cava, Christopher Bauer, Jason H. Moore +1
Electronic health records are an increasingly important resource for understanding the interactions between patient health, environment, and clinical decisions. In this paper we re…
Electronic Health Record Phenotyping with Internally Assessable Performance (PhIAP) using Anchor-Positive and Unlabeled Patients
Lingjiao Zhang, Xiruo Ding, Yanyuan Ma +5
Building phenotype models using electronic health record (EHR) data conventionally requires manually labeled cases and controls. Assigning labels is labor intensive and, for some p…
EBIC: an open source software for high-dimensional and big data biclustering analyses
Patryk Orzechowski, Jason H. Moore
Motivation: In this paper we present the latest release of EBIC, a next-generation biclustering algorithm for mining genetic data. The major contribution of this paper is adding su…
Learning concise representations for regression by evolving networks of trees
William La Cava, Tilak Raj Singh, James Taggart +2
We propose and study a method for learning interpretable representations for the task of regression. Features are represented as networks of multi-type expression trees comprised o…
Gamorithm
Moshe Sipper, Jason H. Moore
Examining games from a fresh perspective we present the idea of game-inspired and game-based algorithms, dubbed "gamorithms".
Where are we now? A large benchmark study of recent symbolic regression methods
Patryk Orzechowski, William La Cava, Jason H. Moore
In this paper we provide a broad benchmarking of recent genetic programming approaches to symbolic regression in the context of state of the art machine learning approaches. We use…