Identifying potentiating events in evolutionary search using replay experiments
2026-08-10 • Neural and Evolutionary Computing
Neural and Evolutionary Computing
AI summaryⓘ
The authors introduce a method called analytical replay experiments to study evolutionary computing. This method involves restarting evolution from different past points to see how likely certain outcomes are and how a population's potential changes over time. They show an example where increased chance of solving problems does not always match better fitness scores. The authors believe this approach can improve understanding of evolution in computing and suggest future research ideas.
evolutionary computingreplay experimentsanalytical replaygenetic programmingevolutionary potentialfitnessproblem-solvingexperimental evolutionpopulation historyevolutionary outcomes
Authors
Austin J. Ferguson, Alexander Lalejini
Abstract
In this work, we introduce analytical replay experiments to the evolutionary computing community. Replay experiments originated in the context of laboratory experimental evolution as an empirical approach to identifying potentiating events that increased the likelihood of an observed evolutionary outcome. By restarting a population's evolution from different historical time points, replay experiments sample the distribution of what could have evolved from different points in time, which allows us to quantify how a population's potential for different evolutionary outcomes changed as a result of that population's history. In this work, we give a step-by-step guide to designing replay experiments for evolutionary computing systems. We then provide a demonstrative example replay experiment that measures how potentiation for problem-solving success changed in an evolved genetic programming population, showing that increases in potential for success do not necessarily correspond with increases in a population's fitness. Broadly, we argue that analytical replay experiments can be a powerful tool for expanding the theoretical foundations of evolutionary computing, and we offer suggestions for promising future research directions enabled by replay experiments.