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Found 10 papers, 5 papers with code
Informed Down-Sampled Lexicase Selection: Identifying productive training cases for efficient problem solving
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.
A suite of diagnostic metrics for characterizing selection schemes
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).
Matchmaker, Matchmaker, Make Me a Match: Geometric, Variational, and Evolutionary Implications of Criteria for Tag Affinity
Genetic programming and artificial life systems commonly employ tag-matching schemes to determine interactions between model components.
SignalGP-Lite: Event Driven Genetic Programming Library for Large-Scale Artificial Life Applications
Event-driven genetic programming representations have been shown to outperform traditional imperative representations on interaction-intensive problems.
An Exploration of Exploration: Measuring the ability of lexicase selection to find obscure pathways to optimality
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.
Exploring Evolved Multicellular Life Histories in a Open-Ended Digital Evolution System
These digital cells were allowed to form and replicate kin groups by selectively adjoining or expelling daughter cells.
Tag-based regulation of modules in genetic programming improves context-dependent problem solving
We demonstrate the functionality of tag-based regulation on a range of program synthesis problems.
Evolving Event-driven Programs with SignalGP
We present SignalGP, a new genetic programming (GP) technique designed to incorporate the event-driven programming paradigm into computational evolution's toolbox.
The Surprising Creativity of Digital Evolution: A Collection of Anecdotes from the Evolutionary Computation and Artificial Life Research Communities
Biological evolution provides a creative fount of complex and subtle adaptations, often surprising the scientists who discover them.
Understanding Evolutionary Potential in Virtual CPU Instruction Set Architectures
We evaluated six different types of architectural features for the virtual CPUs: (1) genetic flexibility: we allowed digital organisms to more precisely modify the function of genetic instructions, (2) memory: we provided an increased number of registers in the virtual CPUs, (3) decoupled sensors and actuators: we separated input and output operations to enable greater control over data flow.
