144 citations · 392 across the 15 of their papers we have counts for
10 papers · 1 filter
Minimum variance threshold for epsilon-lexicase selection
Guilherme Seidyo Imai Aldeia, Fabricio Olivetti de Franca, William G. La Cava
Parent selection plays an important role in evolutionary algorithms, and many strategies exist to select the parent pool before breeding the next generation. Methods often rely on…
Inexact Simplification of Symbolic Regression Expressions with Locality-sensitive Hashing
Guilherme Seidyo Imai Aldeia, Fabricio Olivetti de Franca, William G. La Cava
Symbolic regression (SR) searches for parametric models that accurately fit a dataset, prioritizing simplicity and interpretability. Despite this secondary objective, studies point…
Optimizing fairness tradeoffs in machine learning with multiobjective meta-models
William G. La Cava
Improving the fairness of machine learning models is a nuanced task that requires decision makers to reason about multiple, conflicting criteria. The majority of fair machine learn…
Population Diversity Leads to Short Running Times of Lexicase Selection
Thomas Helmuth, Johannes Lengler, William La Cava
In this paper we investigate why the running time of lexicase parent selection is empirically much lower than its worst-case bound of O(N*C). We define a measure of population dive…
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…
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…