Avida is an artificial life software platform to study the evolutionary biology of self-replicating and evolving computer programs (digital organisms). Avida is under active development by Charles Ofria's Digital Evolution Lab at Michigan State University and was originally designed by Ofria, Chris Adami and C. Titus Brown at Caltech in 1993. The software was inspired by the Tierra system.
Tierra simulated an evolutionary system by introducing computer programs that competed for computer resources, specifically processor (CPU) time and access to main memory. In this respect it was similar to Core Wars, but differed in that the programs being run in the simulation were able to modify themselves, and thereby evolve. Tierra's programs were artificial life organisms.
Unlike Tierra, Avida assigns every digital organism its own protected region of memory, and executes it with a separate virtual CPU. By default, other digital organisms cannot access this memory space, neither for reading nor for writing, and cannot execute code that is not in their own memory space.
A second major difference is that the virtual CPUs of different organisms can run at different speeds, such that one organism executes, for example, twice as many instructions in the same time interval as another organism. The speed at which a virtual CPU runs is determined by a number of factors, but most importantly, by the tasks that the organism performs: logical computations that the organisms can carry out to reap extra CPU speed as bonus.
Use in research
Scientific publications featuring Avida
- C. Adami and C.T. Brown (1994), Evolutionary Learning in the 2D Artificial Life Systems Avida, in: R. Brooks, P. Maes (Eds.), Proc. Artificial Life IV, MIT Press, Cambridge, MA, p. 377-381. arXiv:adap-org/9405003v1
- R. E. Lenski, C. Ofria, T. C. Collier, C. Adami (1999). Genome Complexity, Robustness, and Genetic Interactions in Digital Organisms. Nature 400:661-664. abstract of this article
- C.O. Wilke, J.L. Wang, C. Ofria, R.E. Lenski, and C. Adami (2001). Evolution of Digital Organisms at High Mutation Rate Leads To Survival of the Flattest. Nature 412:331-333.
- R.E. Lenski, C. Ofria, R.T. Pennock, and C. Adami (2003). The Evolutionary Origin of Complex Features. Nature 423:139-145.
- S.S. Chow, C.O. Wilke, C. Ofria, R.E. Lenski, and C. Adami (2004). Adaptive Radiation from Resource Competition in Digital Organisms. Science 305:84-86.
- J. Clune, D. Misevic, C. Ofria, R.E. Lenski, S.F. Elena, and R. Sanjuán. Natural selection fails to optimize mutation rates for long-term adaptation on rugged fitness landscapes. PLoS Computational Biology 4(9): 2008. full text available
- Clune J, Goldsby HJ, Ofria C, and Pennock RT (2011) Selective pressures for accurate altruism targeting: Evidence from digital evolution for difficult-to-test aspects of inclusive fitness theory. Proceedings of the Royal Society. pdf
- Benjamin E. Beckmann, Philip K. McKinley, Charles Ofria (2007). Evolution of an adaptive sleep response in digital organisms. ECAL 2007 pdf