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Related Experiment Videos

Multiple von Neumann computers: an evolutionary approach to functional emergence

H Suzuki1

  • 1Fundamental Research Div., Honda R&D Co., Ltd., Wako Research Center, Saitama, Japan. h_suzuki@f14k.f.rd.honda.co.jp

Artificial Life
|April 1, 1997
PubMed
Summary

This study introduces a novel system where genetic algorithms (GAs) evolve programs for von Neumann computers. GAs accelerate optimization by creating advantageous machine code combinations for enhanced computational function.

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Computational Biology

Background:

  • Von Neumann computer architecture is a foundational model for modern computing.
  • Genetic algorithms (GAs) are optimization techniques inspired by biological evolution.
  • Evolving machine code for complex tasks presents significant computational challenges.

Purpose of the Study:

  • To propose and simulate a novel system integrating von Neumann computers with genetic algorithms.
  • To investigate the emergence of higher computational functions through evolutionary processes.
  • To analyze the performance of genetic algorithms in optimizing computer programs.

Main Methods:

  • A simulated system of multiple von Neumann computers interacting with an environmental database.

Related Experiment Videos

  • Utilization of genetic algorithms (GAs), including mutation and crossover, to evolve machine instruction programs.
  • Examination of evolutionary speed under varying GA parameters, such as mutation rate and population size.

    • Main Results:

    • The system successfully evolved machine code programs enabling computers to derive numbers from the environment.
    • Evolutionary operations, specifically mutation and crossover, led to the emergence of advantageous machine code combinations.
    • Optimal performance was observed at intermediate mutation rates and population sizes, with crossover significantly accelerating optimization.

    Conclusions:

    • The proposed system demonstrates a viable approach for evolving complex computational functions using genetic algorithms.
    • Genetic algorithms, particularly with crossover, can effectively accelerate the optimization of machine instruction programs in a von Neumann architecture.
    • Understanding the interplay of GA parameters is crucial for efficient evolutionary computation in such systems.