CamoEvo – rapid camouflage evolution
We have developed a Genetic Algorithm (GA) that combines human psychophysics experiments with recent advances in GA technology to rapidly evolve target camouflage. The GA allows explicit testing of a range of camouflage strategies and theories, and can simulate important population-level evolutionary strategies (such as polymorphisms).
Background
Camouflage is one of the most common anti-predator strategies in nature. As such it presents one of the most intuitive and long-standing systems for studying the evolution of animal phenotypes. Research over the past 20 years as typically been unable to explore the almost limitless combinations of patterns and colours that could optimise an animal’s camouflage against its surrounds. Particularly given the complex interactions that exist between prey appearance, background appearance, predator vision and cognition.
Accessible and Adaptable
CamoEvo incorporates a range of advanced functions that makes it ideally suited to academic research in visual ecology and evolution, where the user can run experiments to test their own hypotheses. The toolbox has been built to provide user-friendly access to its core features, and is released for free using open-source software.
Publications
We use this tool extensively in our lab as it allows us to test camouflage hypotheses while being able to escape the field’s prior reliance on quite limited prey phenotypes:
Hancock, G.R.A.*, Troscianko, J., (2022) CamoEvo: An open access toolbox for artificial camouflage evolution experiments. Evolution. https://doi.org/10.1111/evo.14476
Briolat, E.S., Hancock, G.R. and Troscianko, J., (2024). Adapting genetic algorithms for artificial evolution of visual patterns under selection from wild predators. Plos One. https://doi.org/10.1371/journal.pone.0295106
Hancock, G. R. A., Cuthill, I. C., & Troscianko, J. (2026). Shining a light on camouflage evolution: using genetic algorithms to determine the effects of geometry and lighting on optimal camouflage. PLoS One, 21(4), e0346231. https://doi.org/10.1371/journal.pone.0346231
Downloads
Updates and changelogs are listed on our GitHub. Included are prepackaged versions of ImageJ with CamoEvo installed.
Requires ImageJ Version 1.52