This document is a companion to our submission to the 2020 Artificial Life conference, ‘Genetic regulation facilitates the evolution of signal-response plasticity in digital organisms’. Here, we give an overview of the repeated signal diagnostic task (mirroring the overview given in the paper), and we provide our data analyses for our repeated signal task experiments. All of our source code for statistical analyses and data visualizations is embedded in this document. Please file an issue or make a pull request on github to report any mistakes or request more explanation.


# Experimental parameters referenced in-text all in one convenient place.
time_steps <- 128
replicates <- 100
population_size <- 1000
generations <- 10000
env_complexities <- c(2, 4, 8, 16)

The repeated signal task is one of several diagnostics we used to evaluate whether genetic regulation faculties contribute to, and potentially detract from, the functionality of evolved SignalGP digital organisms. The repeated signal task, specifically, requires organisms to exhibit signal-response plasticity; that is, they must shift their response to a repeated environmental signal during their lifetime.

The repeated signal task requires organisms to express the appropriate (distinct) response to a single environmental signal each of the \(K\) times that it is repeated. Organisms express reponses by executing one of \(K\) response instructions. For example, if organisms receive four signals from the environment (i.e., \(K=2\)), a maximally fit organism will express Response-1 after the first signal, Response-2 after the second, Response-3 after the third, and Response-4 after the fourth. The figure below depicts examples of optimal behavior in two- and four-signal tasks.