145 citations · 793 across the 36 of their papers we have counts for
20 papers · 1 filter
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…
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…
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…
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.…
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…
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…
A probabilistic and multi-objective analysis of lexicase selection and epsilon-lexicase selection
William La Cava, Thomas Helmuth, Lee Spector +1
Lexicase selection is a parent selection method that considers training cases individually, rather than in aggregate, when performing parent selection. Whereas previous work has de…
Investigating the Parameter Space of Evolutionary Algorithms
Moshe Sipper, Weixuan Fu, Karuna Ahuja +1
The practice of evolutionary algorithms involves the tuning of many parameters. How big should the population be? How many generations should the algorithm run? What is the (tourna…
Ensemble representation learning: an analysis of fitness and survival for wrapper-based genetic programming methods
William La Cava, Jason H. Moore
Recently we proposed a general, ensemble-based feature engineering wrapper (FEW) that was paired with a number of machine learning methods to solve regression problems. Here, we ad…
Toward the automated analysis of complex diseases in genome-wide association studies using genetic programming
Andrew Sohn, Randal S. Olson, Jason H. Moore
Machine learning has been gaining traction in recent years to meet the demand for tools that can efficiently analyze and make sense of the ever-growing databases of biomedical data…
Identifying and Harnessing the Building Blocks of Machine Learning Pipelines for Sensible Initialization of a Data Science Automation Tool
Randal S. Olson, Jason H. Moore
As data science continues to grow in popularity, there will be an increasing need to make data science tools more scalable, flexible, and accessible. In particular, automated machi…
Evaluation of a Tree-based Pipeline Optimization Tool for Automating Data Science
Randal S. Olson, Nathan Bartley, Ryan J. Urbanowicz +1
As the field of data science continues to grow, there will be an ever-increasing demand for tools that make machine learning accessible to non-experts. In this paper, we introduce…