Search Results for author: Matthew Andres Moreno

Found 8 papers, 2 papers with code

Trackable Agent-based Evolution Models at Wafer Scale

no code implementations16 Apr 2024 Matthew Andres Moreno, Connor Yang, Emily Dolson, Luis Zaman

Continuing improvements in computing hardware are poised to transform capabilities for in silico modeling of cross-scale phenomena underlying major open questions in evolutionary biology and artificial life, such as transitions in individuality, eco-evolutionary dynamics, and rare evolutionary events.

Artificial Life

Methods to Estimate Cryptic Sequence Complexity

no code implementations16 Apr 2024 Matthew Andres Moreno

Complexity is a signature quality of interest in artificial life systems.

Artificial Life

Runtime phylogenetic analysis enables extreme subsampling for test-based problems

no code implementations2 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.

Program Synthesis

Phylogeny-informed fitness estimation

no code implementations6 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.

Evolutionary Algorithms

Matchmaker, Matchmaker, Make Me a Match: Geometric, Variational, and Evolutionary Implications of Criteria for Tag Affinity

1 code implementation10 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.

Artificial Life TAG

SignalGP-Lite: Event Driven Genetic Programming Library for Large-Scale Artificial Life Applications

no code implementations1 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.

Artificial Life Benchmarking +1

Exploring Evolved Multicellular Life Histories in a Open-Ended Digital Evolution System

no code implementations20 Apr 2021 Matthew Andres Moreno, Charles Ofria

These digital cells were allowed to form and replicate kin groups by selectively adjoining or expelling daughter cells.

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