Multiple von Neumann Computers: An Evolutionary Approach to Functional Emergence

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@Article{suzuki:1997:mvnc,
  author =       "Hideaki Suzuki",
  title =        "Multiple von Neumann Computers: An Evolutionary
                 Approach to Functional Emergence",
  journal =      "Artificial Life",
  year =         "1997",
  volume =       "3",
  pages =        "121--142",
  month =        "Spring",
  keywords =     "genetic algorithms, genetic programming",
  ISSN =         "1064-5462",
  URL =          "http://www.nis.atr.jp/~hsuzuki/papers/1997_AL.ps.gz",
  abstract =     "A novel system composed of multiple von Neumann
                 computers and an appropriate problem environment is
                 proposed and simulated. Each computer has a memory to
                 store the machine instruction program, and when a
                 program is executed, a series of machine codes in the
                 memory is sequentially decoded, leading to register
                 operations in the central processing unit (CPU). By
                 means of these operations, the computer not only can
                 handle its generally used registers but also can read
                 and write the environmental database. Simulation is
                 driven by genetic algorithms (GAs) performed on the
                 population of program memories. Mutation and crossover
                 create program diversity in the memory, and selection
                 facilitates the reproduction of appropriate programs.
                 Through these evolutionary operations, advantageous
                 combinations of machine codes are created and fixed in
                 the population one by one, and the higher function,
                 which enables the computer to calculate an appropriate
                 number from the environment, finally emerges in the
                 program memory. In the latter half of the article, the
                 performance of GAs on this system is studied. Under
                 different sets of parameters, the evolutionary speed,
                 which is determined by the time until the domination of
                 the final program, is examined and the conditions for
                 faster evolution are clarified. At an intermediate
                 mutation rate and at an intermediate population size,
                 crossover helps create novel advantageous sets of
                 machine codes and evidently accelerates optimisation by
                 GAs.",
  notes =        "Artificial Life Journal

                 PMID: 9212493",
}

Genetic Programming entries for Hideaki Suzuki

Citations