26 citations · 85 across the 10 of their papers we have counts for
10 papers
Generational Computation Reduction in Informal Counterexample-Driven Genetic Programming
Thomas Helmuth, Edward Pantridge, James Gunder Frazier +1
Counterexample-driven genetic programming (CDGP) uses specifications provided as formal constraints to generate the training cases used to evaluate evolving programs. It has also b…
Solving Novel Program Synthesis Problems with Genetic Programming using Parametric Polymorphism
Edward Pantridge, Thomas Helmuth
Contemporary genetic programming (GP) systems for general program synthesis have been primarily concerned with evolving programs that can manipulate values from a standard set of p…
Probabilistic Lexicase Selection
Li Ding, Edward Pantridge, Lee Spector
Lexicase selection is a widely used parent selection algorithm in genetic programming, known for its success in various task domains such as program synthesis, symbolic regression,…
Functional Code Building Genetic Programming
Edward Pantridge, Thomas Helmuth, Lee Spector
General program synthesis has become an important application area for genetic programming (GP), and for artificial intelligence more generally. Code Building Genetic Programming (…
Independence Tests Without Ground Truth for Noisy Learners
Andrés Corrada-Emmanuel, Edward Pantridge, Eddie Zahrebelski +2
Exact ground truth invariant polynomial systems can be written for arbitrarily correlated binary classifiers. Their solutions give estimates for sample statistics that require know…
Code Building Genetic Programming
Edward Pantridge, Lee Spector
In recent years the field of genetic programming has made significant advances towards automatic programming. Research and development of contemporary program synthesis methods, su…
Algebraic Ground Truth Inference: Non-Parametric Estimation of Sample Errors by AI Algorithms
Andrés Corrada-Emmanuel, Edward Pantridge, Edward Zahrebelski +2
Binary classification is widely used in ML production systems. Monitoring classifiers in a constrained event space is well known. However, real world production systems often lack…
Error Correcting Algorithms for Sparsely Correlated Regressors
Andrés Corrada-Emmanuel, Edward Zahrebelski, Edward Pantridge
Autonomy and adaptation of machines requires that they be able to measure their own errors. We consider the advantages and limitations of such an approach when a machine has to mea…
Lexicase Selection of Specialists
Thomas Helmuth, Edward Pantridge, Lee Spector
Lexicase parent selection filters the population by considering one random training case at a time, eliminating any individuals with errors for the current case that are worse than…
TensorFlow Enabled Genetic Programming
Kai Staats, Edward Pantridge, Marco Cavaglia +2
Genetic Programming, a kind of evolutionary computation and machine learning algorithm, is shown to benefit significantly from the application of vectorized data and the TensorFlow…