Fitness distributions (landscapes) of programs tend to a limit as they get bigger. Markov minorization gives upper bounds ((15.3 + 2.30m) / log I) on the length of program run on random or average computing devices. I is the size of the instruction set and m size of output register. Almost all programs are constants. Convergence is exponential with 90 % of programs of length 1.6 n2 N yielding constants (n = size input register and size of memory = N). This is supported by experiment. 1 The Amorphous or Average Computer In Computer Science we are used to the notion that computers are highly designed, precision engineered artifacts. Nevertheless we can theoretically analyse more amorphous computing devices. Indeed nanotechnology may be a route to their practical construction and use. Consider an abstract machine whose instruction set, rather than being designed, is chosen at random. We can consider random linear computer programs as Markov processes which move the computer from one state to another [1]. 1.1 Convergence of Outputs We start by considering what happens when a single instruction is executed. Then consider two consecutive instructions, then a program of a instructions and so on
