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A Minimal Model for Biological Evolution and Other Adaptive Processes

Stephen Wolfram explores simple models of biological organisms as computational systems. A study of progressive development, multiway graphs of all possible paths and the need for narrowing the framework space.

Idealized neural networks were introduced by Warren McCulloch and Walter Pitts in 1943, and soon the idea emerged (notably in the work of Donald Hebb in 1949) that learning in such systems could occur through a kind of adaptive evolution process. A particularly elaborate example was work by Nils Barricelli on what he called “numeric evolution” in which a fairly complicated numerical-cellular-automaton-like “competition between organisms” program with “randomness” injected from details of data layout in computer memory showed what he claimed to be biological-evolution-like phenomena (such as symbiosis and parasitism). Then in the 1960s, John Holland(who had at first studied learning in neural nets, and was then influenced by Arthur Burks who had worked on cellular automata with von Neumann) suggested representing what amounted to programs by simple strings of symbols that could readily be modified like genomic sequences.

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