no code implementations • 8 Jun 2023 • 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 primitive data types and simple indexed data structures.
no code implementations • 14 Apr 2023 • Ryan Boldi, Ashley Bao, Martin Briesch, Thomas Helmuth, Dominik Sobania, Lee Spector, Alexander Lalejini
We verified that down-sampling can benefit the problem-solving success of both fitness-proportionate and tournament selection.
no code implementations • 4 Apr 2023 • Ryan Boldi, Alexander Lalejini, Thomas Helmuth, Lee Spector
We present an analysis of the loss of population-level test coverage induced by different down-sampling strategies when combined with lexicase selection.
no code implementations • 4 Jan 2023 • Ryan Boldi, Martin Briesch, Dominik Sobania, Alexander Lalejini, Thomas Helmuth, Franz Rothlauf, Charles Ofria, Lee Spector
Random down-sampled lexicase selection evaluates individuals on only a random subset of the training cases allowing for more individuals to be explored with the same amount of program executions.
no code implementations • 23 Aug 2022 • Li Ding, Ryan Boldi, Thomas Helmuth, Lee Spector
Lexicase selection is a semantic-aware parent selection method, which assesses individual test cases in a randomly-shuffled data stream.
no code implementations • 9 Jun 2022 • 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.
1 code implementation • 31 May 2022 • Ryan Boldi, Thomas Helmuth, Lee Spector
Although this down-sampling procedure has been shown to significantly improve performance across a variety of problems, it does not seem to do so due to encouraging adaptability through environmental change.
no code implementations • 13 Apr 2022 • 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).
no code implementations • 10 Jun 2021 • Thomas Helmuth, Lee Spector
Lexicase selection, by contrast, selects on the basis of performance on random sequences of training cases; this has been shown to enhance problem-solving power in many circumstances.
no code implementations • 10 Jun 2021 • Thomas Helmuth, Peter Kelly
In this paper, we describe the 25 new general program synthesis benchmark problems that make up PSB2, a new benchmark suite.
1 code implementation • 22 May 2019 • 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 the best error in the selection pool, until a single individual remains.
no code implementations • 22 May 2019 • Lia Jundt, Thomas Helmuth
Lexicase selection and novelty search, two parent selection methods used in evolutionary computation, emphasize exploring widely in the search space more than traditional methods such as tournament selection.
1 code implementation • 15 Sep 2017 • William La Cava, Thomas Helmuth, Lee Spector, Jason H. Moore
Lexicase selection is a parent selection method that considers training cases individually, rather than in aggregate, when performing parent selection.