no code implementations • 9 Apr 2024 • Jack Garbus, Thomas Willkens, Alexander Lalejini, Jordan Pollack
Co-evolution is a powerful problem-solving approach.
no code implementations • 2 Feb 2024 • Alexander Lalejini, Marcos Sanson, Jack Garbus, Matthew Andres Moreno, Emily Dolson
We introduce phylogeny-informed subsampling, a new class of subsampling methods that exploit runtime phylogenetic analyses for solving test-based problems.
no code implementations • 20 Sep 2023 • Emily Dolson, Alexander Lalejini
We then demonstrate that this approach can be successfully applied to a complex genetic programming problem.
no code implementations • 6 Jun 2023 • Alexander Lalejini, Matthew Andres Moreno, Jose Guadalupe Hernandez, Emily Dolson
Thus far, phylogenetic analyses have primarily been applied as post-hoc analyses used to deepen our understanding of existing evolutionary algorithms.
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.
2 code implementations • 29 Apr 2022 • Jose Guadalupe Hernandez, Alexander Lalejini, Charles Ofria
We consider exploitation both with and without constraints, and we divide exploration into two aspects: diversity exploration (the ability to simultaneously explore multiple pathways) and valley-crossing exploration (the ability to cross wider and wider fitness valleys).
1 code implementation • 28 Aug 2021 • Jose Guadalupe Hernandez, Alexander Lalejini, Emily Dolson
While these metrics are informative, we hypothesize that other diversity metrics are more strongly predictive of success.
1 code implementation • 10 Aug 2021 • Matthew Andres Moreno, Alexander Lalejini, Charles Ofria
Genetic programming and artificial life systems commonly employ tag-matching schemes to determine interactions between model components.
1 code implementation • 1 Aug 2021 • Matthew Andres Moreno, Santiago Rodriguez Papa, Alexander Lalejini, Charles Ofria
Event-driven genetic programming representations have been shown to outperform traditional imperative representations on interaction-intensive problems.
1 code implementation • 20 Jul 2021 • Jose Guadalupe Hernandez, Alexander Lalejini, Charles Ofria
We use our exploration diagnostic to investigate the exploratory capacity of lexicase selection and several of its variants: epsilon lexicase, down-sampled lexicase, cohort lexicase, and novelty-lexicase.
1 code implementation • 16 Dec 2020 • Alexander Lalejini, Matthew Andres Moreno, Charles Ofria
We demonstrate the functionality of tag-based regulation on a range of program synthesis problems.
1 code implementation • 15 Apr 2018 • Alexander Lalejini, Charles Ofria
We present SignalGP, a new genetic programming (GP) technique designed to incorporate the event-driven programming paradigm into computational evolution's toolbox.