At the MSU Digital Evolution Laboratory (Devolab), we perform experimental studies on digital organisms with the twin goals of improving our understanding of how natural evolution works, and applying this knowledge to solving computational, engineering, and biological problems.
Hello, world, we’re back! The Devolab (i.e. Dr. Charles Ofria’s research lab) has been up to lots of research, but not much blogging – until now. We’ve got lots of plans for discussing interesting papers, ongoing research, behind-the-scenes looks at grad student life, and much more. For now, plan on seeing new posts on Tuesdays.
To start things off, we thought you’d like to know what we’ve been up to in the past year or so, therefore here is a list of the recent publications from the lab. Going forward, we’ll aim to have posts with high-level discussions of our new papers, and slowly fill in with posts about some of the more exciting previous papers. Without further ado, we present research:
- Heather J. Goldsby, David B. Knoester, Benjamin Kerr, and Charles Ofria. The Effect of Conflicting Pressures on The Evolution of Division of Labor, PLoS One, 2014. link
- Anya E. Johnson, Heather J. Goldsby, Sherri Goings, and Charles Ofria. The Evolution of Kin Inclusivity Levels, GECCO, 2014. link
- Anya E. Johnson, Eli Strauss, Rodney Pickett, Christoph Adami, Ian Dworkin, and Heather J. Goldsby. More Bang for Your Buck: Quorum-Sensing Capabilities Improve the Efficacy of Suicidal Altruism, ALIFE 2014. link extended
- Heather J. Goldsby, David B. Knoester, Charles Ofria, and Benjamin Kerr. The Evolutionary Origin of Somatic Cells Under the Dirty Work Hypothesis, PLoS Biology, 2014. link
- David M. Bryson, Aaron P. Wagner, and Charles Ofria.There and back again: gene-processing hardware for the evolution and robotic deployment of robust navigation strategies, GECCO, 2014. link
- Aaron P. Wagner, Luis Zaman, Ian Dworkin, and Charles Ofria. Behavioral Strategy Chases Promote the Evolution of Prey Intelligence, arXiv, 2014. link
- David B. Knoester, Heather J. Goldsby, and Philip K. McKinley. Genetic Variation and the Evolution of Consensus in Digital Organisms, IEEE Transactions on Evolutionary Computation, 2013. link
- David M. Bryson and Charles Ofria.Understanding Evolutionary Potential in Virtual CPU Instruction Set Architectures, PLoS One, 2013. link
- Arthur W. Covert, Richard E. Lenski, Claus O. Wilke, and Charles Ofria. Experiments on the role of deleterious mutations as stepping stones in adaptive evolution, PNAS, 2013. link
- Laura M. Grabowski, David M. Bryson, Fred C. Dyer, Robert T. Pennock, and Charles Ofria. A Case Study of the De Novo Evolution of a Complex Odometric Behavior in Digital Organisms, PLoS One, 2013. link
- Miguel A. Fortuna, Luis Zaman, Aaron P. Wagner, and Charles Ofria. Evolving Digital Ecological Networks, PLoS Computational Biology, 2013. link
- Christopher H. Chandler, Charles Ofria, and Ian Dworkin. Runaway Sexual Selection Leads to Good Genes, Evolution, 2013. link
- Heather J. Goldsby, Anna Dornhaus, Benjamin Kerr, and Charles Ofria. Task-Switching Costs Promote the Evolution of Division of Labor and Shifts in Individuality, Proceedings of the National Academy of Science, 2012. link
I tried to make sure that all the paper links are open-access, so let me know if you’d like to read a paper that you can’t access above. As always, we’d love to discuss all of this research with you, so let us know if there is a particular one you’d like a more in-depth post on.
Several new Devolab publications have been accepted to appear at Artificial Life 13.
“Digital Evolution Exhibits Surprising Robustness to Poor Design Decisions” by David M. Bryson and Charles Ofria
When designing an evolving software system, a researcher must set many aspects of the representation and inevitably make arbitrary decisions. Here we explore the consequences of poor design decisions in the development of a virtual instruction set in digital evolution systems. We evaluate the introduction of three different severities of poor choices. (1) functionally neutral instructions that water down mutational options, (2) actively deleterious instructions, and (3) a lethal die instruction. We further examine the impact of a high level of neutral bloat on the short term evolutionary potential of genotypes experiencing environmental change. We observed surprising robustness to these poor design decisions across all seven environments designed to analyze a wide range challenges. Analysis of the short term evolutionary potential of genotypes from the principal line of descent of case study populations demonstrated that the negative effects of neutral bloat in a static environment are compensated by retention of evolutionary potential during environmental change.
“Finger-painting Fitness Landscapes: An Interactive Tool for Exploring Complex Evolutionary Dynamics.” by Luis Zaman, Charles Ofria, and Richard E. Lenski
Evolution involves only a few simple processes, yet the resulting dynamics are surprisingly rich and complex. Sewall Wright developed the metaphor of fitness landscapes to provide deeper insight into the complex workings of evolution. Here we extend that metaphor by visualizing in real time the dynamic processes that drive evolution. We allow viewers to construct fitness landscapes interactively while also varying key parameters including population size, mutation effect size, mode of reproduction (asexual or sexual), and densitydependent selection. This application is both mechanistic and visual, and it thereby allows the active exploration of evolutionary processes. We walk the reader through several exercises including both simple activities potentially suitable for education and examples of deeply conceptual topics that remain the focus of current research in evolutionary biology.
“Evolutionary Potential is Maximized at Intermediate Diversity Levels” by Bess L. Walker and Charles Ofria
Diversity in a population is often cited as a major facilitator for the evolution of new complex features. The intuition behind this dynamic is that if a population is exploring multiple regions of a fitness landscape, more opportunities exist to find new functionality. We use the digital evolution software platform Avida to explore the effect of multiple limited resources on phenotypic Shannon diversity and, in turn, on evolvability of populations. We show that Shannon diversity peaks at intermediate levels of resource availability to the population, and we map the evolvability of a complex computational task on this availability-diversity gradient. While the evolvability of the complex task is highest at intermediate availabilities, it does not peak at the same resource inflow level as Shannon diversity, and it is more robust than diversity in its response to inflow level. These results indicate that while phenotypic Shannon diversity may play into the evolution of complex features, the selective pressures caused by diversity cannot be the only — or indeed even the main — pressures behind such evolution.