21 citations · 77 across the 14 of their papers we have counts for
14 papers
Identifying potentiating events in evolutionary search using replay experiments
Austin J. Ferguson, Alexander Lalejini
In this work, we introduce analytical replay experiments to the evolutionary computing community. Replay experiments originated in the context of laboratory experimental evolution…
Runtime phylogenetic analysis enables extreme subsampling for test-based problems
Alexander Lalejini, Marcos Sanson, Jack Garbus +2
A phylogeny describes the evolutionary history of an evolving population. Evolutionary search algorithms can perfectly track the ancestry of candidate solutions, illuminating a pop…
Reachability Analysis for Lexicase Selection via Community Assembly Graphs
Emily Dolson, Alexander Lalejini
Fitness landscapes have historically been a powerful tool for analyzing the search space explored by evolutionary algorithms. In particular, they facilitate understanding how easil…
Phylogeny-informed fitness estimation
Alexander Lalejini, Matthew Andres Moreno, Jose Guadalupe Hernandez +1
Phylogenies (ancestry trees) depict the evolutionary history of an evolving population. In evolutionary computing, a phylogeny can reveal how an evolutionary algorithm steers a pop…
A Static Analysis of Informed Down-Samples
Ryan Boldi, Alexander Lalejini, Thomas Helmuth +1
We present an analysis of the loss of population-level test coverage induced by different down-sampling strategies when combined with lexicase selection. We study recorded populati…
Untangling the Effects of Down-Sampling and Selection in Genetic Programming
Ryan Boldi, Ashley Bao, Martin Briesch +4
Genetic programming systems often use large training sets to evaluate the quality of candidate solutions for selection, which is often computationally expensive. Down-sampling trai…
Informed Down-Sampled Lexicase Selection: Identifying productive training cases for efficient problem solving
Ryan Boldi, Martin Briesch, Dominik Sobania +5
Genetic Programming (GP) often uses large training sets and requires all individuals to be evaluated on all training cases during selection. Random down-sampled lexicase selection…
A suite of diagnostic metrics for characterizing selection schemes
Jose Guadalupe Hernandez, Alexander Lalejini, Charles Ofria
Benchmark suites are crucial for assessing the performance of evolutionary algorithms, but the constituent problems are often too complex to provide clear intuition about an algori…
What can phylogenetic metrics tell us about useful diversity in evolutionary algorithms?
Jose Guadalupe Hernandez, Alexander Lalejini, Emily Dolson
It is generally accepted that "diversity" is associated with success in evolutionary algorithms. However, diversity is a broad concept that can be measured and defined in a multitu…
Matchmaker, Matchmaker, Make Me a Match: Geometric, Variational, and Evolutionary Implications of Criteria for Tag Affinity
Matthew Andres Moreno, Alexander Lalejini, Charles Ofria
Genetic programming and artificial life systems commonly employ tag-matching schemes to determine interactions between model components. However, the implications of criteria used…
SignalGP-Lite: Event Driven Genetic Programming Library for Large-Scale Artificial Life Applications
Matthew Andres Moreno, Santiago Rodriguez Papa, Alexander Lalejini +1
Event-driven genetic programming representations have been shown to outperform traditional imperative representations on interaction-intensive problems. The event-driven approach o…
An Exploration of Exploration: Measuring the ability of lexicase selection to find obscure pathways to optimality
Jose Guadalupe Hernandez, Alexander Lalejini, Charles Ofria
Parent selection algorithms (selection schemes) steer populations through a problem's search space, often trading off between exploitation and exploration. Understanding how select…
Tag-based regulation of modules in genetic programming improves context-dependent problem solving
Alexander Lalejini, Matthew Andres Moreno, Charles Ofria
We introduce and experimentally demonstrate the utility of tag-based genetic regulation, a new genetic programming (GP) technique that allows programs to dynamically adjust which c…
Evolving Event-driven Programs with SignalGP
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. Event-driven pro…