Zobrazeno 1 - 10
of 16
pro vyhledávání: '"Pantridge, Edward"'
Counterexample-driven genetic programming (CDGP) uses specifications provided as formal constraints to generate the training cases used to evaluate evolving programs. It has also been extended to combine formal constraints and user-provided training
Externí odkaz:
http://arxiv.org/abs/2408.12604
Autor:
Pantridge, Edward, Helmuth, Thomas
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 primitive data types and simple indexed data structures. In contrast, h
Externí odkaz:
http://arxiv.org/abs/2306.04839
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, and machine learning. Due to its non-parametric and recursive nature,
Externí odkaz:
http://arxiv.org/abs/2305.11681
General program synthesis has become an important application area for genetic programming (GP), and for artificial intelligence more generally. Code Building Genetic Programming (CBGP) is a recently introduced GP method for general program synthesis
Externí odkaz:
http://arxiv.org/abs/2206.04561
Autor:
Corrada-Emmanuel, Andrés, Pantridge, Edward, Zahrebelski, Eddie, Chaganti, Aditya, Simeonov, Simeon
Exact ground truth invariant polynomial systems can be written for arbitrarily correlated binary classifiers. Their solutions give estimates for sample statistics that require knowledge of the ground truth of the correct labels in the sample. Of thes
Externí odkaz:
http://arxiv.org/abs/2010.15662
Autor:
Pantridge, Edward, Spector, Lee
In recent years the field of genetic programming has made significant advances towards automatic programming. Research and development of contemporary program synthesis methods, such as PushGP and Grammar Guided Genetic Programming, can produce progr
Externí odkaz:
http://arxiv.org/abs/2008.03649
Autor:
Corrada-Emmanuel, Andrés, Pantridge, Edward, Zahrebelski, Edward, Chaganti, Aditya, Simeonov, Simeon
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 the ground truth these methods require. Privacy concerns may also requ
Externí odkaz:
http://arxiv.org/abs/2006.08312
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 measure the error in a regression task. How can a machine measure the err
Externí odkaz:
http://arxiv.org/abs/1906.07291
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 the best error in the selection pool, until a single individual remai
Externí odkaz:
http://arxiv.org/abs/1905.09372
Publikováno v:
Proceedings of the Genetic and Evolutionary Computation Conference (GECCO) Companion, ACM 2017, pp. 1872-1879
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 numerical computation library on both CPU and GPU architectures. The
Externí odkaz:
http://arxiv.org/abs/1708.03157