The Avida Digital Evolution Platform is an open-source artificial life system that has been used to conduct a wide range of studies on evolutionary dynamics . In Avida, self-replicating computer programs compete for space on a lattice of cells. When an organism reproduces, its offspring is placed in a nearby cell (or in a random cell if the population is well-mixed), replacing any previous occupant of that cell.

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Versions

Avida 2

Avida 2 is hosted on GitHub at https://github.com/devosoft/avida. Currently, this is the most actively used version of Avida. It allows a wide-range of experiments to be performed purely by editing the configuration files, making it relatively accessible to non-programmers.

Avida 3

Avida 3 only ever existed in prototype form, but was a valuable step towards re-conceptualizing how to better organize the Avida codebase. It is noted here to explain the version numbers.

Avida 4

Avida 4 is a highly efficient re-implementation of Avida that prioritizes speed over friendliness to new users. It is available on GitHub at https://github.com/dknoester/avida4.

Avida 5

This version is currently under active development by Charles Ofria and has not yet been released. It seeks to unite the efficiency of Avida 4 with the usability of Avida 2.

Avida-ED

The educational version of Avida, which has been used in a wide range of classrooms in high schools and colleges. It lets students conduct their own digital evolution experiments, facilitating improved understanding of evolution as evidence-based science . Curricula and lesson plans have been developed to accompany Avida-ED , and it runs entirely in a browser to facilitate easy classroom use.

The Avida-ED project was the recipient of the 2017 ISAL Education and Outreach Award.

Avida-ED is available at https://avida-ed.msu.edu/app/AvidaED.html

Studies using Avida

Organized by primary topic area.

Complexity

The evolutionary origin of complex features: This landmark paper used Avida to show that evolution is capable of producing complex features. Specifically, such features can arise when it is advantageous to first evolve a series of simpler “building block” features that are related to the more complex ones . Prior to this paper and some related work, creationists had argued that such a process was impossible.

Other papers on complexity and complex features include:

  • Evolution of biological complexity
  • Genome complexity, robustness and genetic interactions in digital organisms
  • Historical and contingent factors affect re-evolution of a complex feature lost during mass extinction in communities of digital organisms
  • On the Gradual Evolution of Complexity and the Sudden Emergence of Complex Features
  • The MODES Toolbox: Measurements of Open-Ended Dynamics in Evolving Systems

Phenotypic plasticity

Avida has been used to investigate a number of questions about phenotypic plasticity. Early work found that two different types of phenotypic plasticity evolve in Avida when the population is subjected to changing environments: static plasticity and dynamic plasticity . Statically-plastic organisms exploit a clever algorithm in which they are able to execute the exact same sequence of code in both environments and still be successful. Dynamically-plastic organisms, in contrast, use the kind of logical flow-control that human programmers would expect in such a circumstance, sensing the environment and executing different code based on what they sense.

Subsequent research explored the exact sequences of evolutionary “stepping stones” that lead to the evolution of phenotypic plasticity . By killing organisms that evolved these stepping stones and observing the effect on the evolution of phenotypic plasticity, researchers were able to show that these intermediate steps were important to evolving phenotypic plasticity. Avida has also been used to study the effect of having evolved phenotypic plasticity on the rest of the evolutionary processes, finding that it can stabilize evolution in fluctuating environments .

Parasitism

Parasites were introduced into Avida by Luis Zaman and were shown to increase the population diversity of hosts as well as the final complexity achieved by hosts beyond what would be expected from a simple ‘arms race’ . There is some variation in the exact functionality of the parasites, but they generally operate as a parallel thread to within the host, executing their own genomes with the host’s energy. A parasite is able to infect a host if they complete the same computational task. Once infected, they automatically steal a percentage of the host’s energy, usually 80%.

Mutations

Avida has been used to study the effect and evolution of mutation rates, sizes, and types. Papers on this include:

  • Evolution of digital organisms at high mutation rates leads to survival of the flattest
  • Natural Selection Fails to Optimize Mutation Rates for Long-Term Adaptation on Rugged Fitness Landscapes
  • Experiments on the role of deleterious mutations as stepping stones in adaptive evolution
  • Effects of Population Size and Mutation Rate on the Evolution of Mutational Robustness

Sexual Reproduction/Recombination

Avida has been used to study the effect and evolution of sexual reproduction and sexual recombination. Papers on this include:

Cooperation/Altruism/Kin Selection

Avida has been used to study the effect and evolution of cooperation, altruism, and kin selection. Papers on this include:

  • Suicidal selection: Programmed cell death can evolve in unicellular organisms due solely to kin selection
  • Selective pressures for accurate altruism targeting: evidence from digital evolution for difficult-to-test aspects of inclusive fitness theory
  • Kin-Selection: The Rise and Fall of Kin-Cheaters
  • Directed Evolution of Communication and Cooperation in Digital Organisms
  • Evolution of Cooperative Information Gathering in Self-Replicating Digital Organisms

Major Evolutionary Transitions

Avida has been used to study major evolutionary transitions. Papers on this include:

  • Task-switching costs promote the evolution of division of labor and shifts in individuality
  • The Evolutionary Origin of Somatic Cells under the Dirty Work Hypothesis
  • Using group selection to evolve leadership in populations of self-replicating digital organisms
  • Evolution of division of labor in genetically homogenous groups
  • The Effect of Conflicting Pressures on the Evolution of Division of Labor
  • Toward Open-Ended Fraternal Transitions in Individuality

Other Papers

There are many more papers using Avida, listed below.

  • Selective pressures on genomes in molecular evolution
  • Adaptive Radiation from Resource Competition in Digital Organisms
  • Ecological Specialization and Adaptive Decay in Digital Organisms.
  • Harnessing Digital Evolution
  • Evolution of stable ecosystems in populations of digital organisms
  • Digital Evolution of Behavioral Models for Autonomic Systems
  • Selective Press Extinctions, but Not Random Pulse Extinctions, Cause Delayed Ecological Recovery in Communities of Digital Organisms
  • Evolution of Genetic Organization in Digital Organisms
  • Early Evolution of Memory Usage in Digital Organisms.
  • The genotype-phenotype map of an evolving digital organism
  • A Comparison of the Effects of Random and Selective Mass Extinctions on Erosion of Evolutionary History in Communities of Digital Organisms
  • Cooperative network construction using digital germlines
  • Evolution of an Adaptive Sleep Response in Digital Organisms
  • Ontogeny Tends to Recapitulate Phylogeny in Digital Organisms.
  • Evolution of Differentiated Expression Patterns in Digital Organisms
  • Using Avida to Test the Effects of Natural Selection on Phylogenetic Reconstruction Methods
  • The Evolution of Evolvability: Changing Environments Promote Rapid Adaptation in Digital Organisms
  • Evolutionary potential is maximized at intermediate diversity levels
  • Causes vs Benefits in the Evolution of Prey Grouping
  • Cockroaches, drunkards, and climbers: Modeling the evolution of simple movement strategies using digital organisms
  • On the evolution of motility and intelligent tactic response
  • Measuring Biological Complexity in Digital Organisms
  • Fluctuating environments select for short-term phenotypic variation leading to long-term exploration
  • Understanding Evolutionary Potential in Virtual CPU Instruction Set Architectures
  • A Case Study of the De Novo Evolution of a Complex Odometric Behavior in Digital Organisms
  • The evolution of temporal polyethism
  • Gene duplications drive the evolution of complex traits and regulation
  • The effect of natural selection on the performance of maximum parsimony
  • Clever creatures: Case studies of evolved digital organisms.
  • Evolution of leader election in populations of self-replicating digital organisms
  • Evolution of robust data distribution among digital organisms
  • The Evolutionary Origin of Associative Learning
  • Improved adaptation in exogenously and endogenously changing environments
  • Investigations into the evolutionary origin of navigation and learning
  • The Effects of Evolution and Spatial Structure on Diversity in Biological Reserves
  • Spatial resource heterogeneity creates local hotspots of evolutionary potential

References

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Wilke, C. O., Wang, J. L., Ofria, C., Lenski, R. E., & Adami, C. (2001). Evolution of digital organisms at high mutation rates leads to survival of the flattest. Nature, 412(6844), 331–333. https://doi.org/10/dqhp75
Vostinar, A. E., Goldsby, H. J., & Ofria, C. (2019). Suicidal selection: Programmed cell death can evolve in unicellular organisms due solely to kin selection. Ecology and Evolution, 9(16), 9129–9136. https://doi.org/10/gk9zjr
Zaman, L., Meyer, J. R., Devangam, S., Bryson, D. M., Lenski, R. E., & Ofria, C. (2014). Coevolution Drives the Emergence of Complex Traits and Promotes Evolvability. PLOS Biology, 12(12), e1002023. https://doi.org/10/xrq
Zaman, L., Devangam, S., & Ofria, C. (2011). Rapid host-parasite coevolution drives the production and maintenance of diversity in digital organisms. Proceedings of the 13th Annual Conference on Genetic and Evolutionary Computation, 219–226. https://doi.org/10/dtjztd
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Yedid, G., Ofria, C. A., & Lenski, R. E. (2008). Historical and contingent factors affect re-evolution of a complex feature lost during mass extinction in communities of digital organisms. Journal of Evolutionary Biology, 21(5), 1335–1357. https://doi.org/10/d2w4z3
Grabowski, L. M., Bryson, D. M., Dyer, F. C., Pennock, R. T., & Ofria, C. (2013). A Case Study of the De Novo Evolution of a Complex Odometric Behavior in Digital Organisms. PLOS ONE, 8(4), e60466. https://doi.org/10/f4sm6n
Grabowski, L. M., Elsberry, W. R., Ofria, C., & Pennock, R. T. (2008). On the evolution of motility and intelligent tactic response. Proceedings of the 10th Annual Conference on Genetic and Evolutionary Computation, 209–216. https://doi.org/10/dvqc7g
Bryson, D. M., & Ofria, C. (2013). Understanding Evolutionary Potential in Virtual CPU Instruction Set Architectures. PLOS ONE, 8(12), e83242. https://doi.org/10/gkngpg
Canino-Koning, R., Wiser, M. J., & Ofria, C. (2019). Fluctuating environments select for short-term phenotypic variation leading to long-term exploration. PLOS Computational Biology, 15(4), e1006445. https://doi.org/10/gk9zzw
Elsberry, W. R., Grabowski, L. M., Ofria, C., & Pennock, R. T. (2009). Cockroaches, drunkards, and climbers: Modeling the evolution of simple movement strategies using digital organisms. 2009 IEEE Symposium on Artificial Life, 92–99. https://doi.org/10/cwzcnx
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Chandler, C. H., Ofria, C., & Dworkin, I. (2013). Runaway Sexual Selection Leads to Good Genes. Evolution, 67(1), 110–119. https://doi.org/10/f4kgfb
Biswas, R., Bryson, D., Ofria, C., & Wagner, A. (2014). Causes vs Benefits in the Evolution of Prey Grouping. 641–648. https://doi.org/10.1162/978-0-262-32621-6-ch103
Beckmann, B. E., McKinley, P. K., Knoester, D. B., & Ofria, C. (2007). Evolution of Cooperative Information Gathering in Self-Replicating Digital Organisms. First International Conference on Self-Adaptive and Self-Organizing Systems (SASO 2007), 65–76. https://doi.org/10/ddn64f
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Hagstrom, G. I., Hang, D. H., Ofria, C., & Torng, E. (2004). Using Avida to Test the Effects of Natural Selection on Phylogenetic Reconstruction Methods. Artificial Life, 10(2), 157–166. https://doi.org/10/ck86c2
Clune, J., Pennock, R. T., Ofria, C., & Lenski, R. E. (2012). Ontogeny Tends to Recapitulate Phylogeny in Digital Organisms. The American Naturalist, 180(3), E54–E63. https://doi.org/10/f36b9g
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Moreno, M. A., & Ofria, C. (2019). Toward Open-Ended Fraternal Transitions in Individuality. Artificial Life, 25(2), 117–133. https://doi.org/10/gk9zz6
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Lark, A., Richmond, G., Mead, L. S., Smith, J. J., & Pennock, R. T. (2018). Exploring the Relationship between Experiences with Digital Evolution and Students’ Scientific Understanding and Acceptance of Evolution. The American Biology Teacher, 80(2), 74–86. https://doi.org/10/gczs8x
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