From evolutionary computation to the evolution of things

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Evolution has provided a source of inspiration for algorithm designers since the birth of computers. The resulting field, evolutionary computation, has been successful in solving engineering tasks ranging in outlook from the molecular to the astronomical. Today, the field is entering a new phase as evolutionary algorithms that take place in hardware are developed, opening up new avenues towards autonomous machines that can adapt to their environment. We discuss how evolutionary computation compares with natural evolution and what its benefits are relative to other computing approaches, and we introduce the emerging area of artificial evolution in physical systems.

At a glance


  1. The principal diagram of evolutionary algorithms.
    Figure 1: The principal diagram of evolutionary algorithms.

    The initialization process seeds the search with a population of randomly created solutions. After this the algorithm enters a loop of evaluating the current generation of solutions, selecting some to act as the basis for the next generation, and then creating new solutions through variation (mutation or crossover). Periodically, the algorithm checks to see whether user-specified termination criteria are met — such as reaching a desired level of fitness, or undergoing a certain number of generations without improvement.

  2. Two major transitions in the history of artificial evolution.
    Figure 2: Two major transitions in the history of artificial evolution.

    In the twentieth century computer technology enabled artificial Darwinian processes in silico — the evolution of digital entities. In the twenty-first century, developments in robotics, materials science and 3D printing will enable the evolution of physical artefacts or machines.


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  1. VU University Amsterdam, de Boelelaan 1081a, 1081HV Amsterdam, the Netherlands.

    • Agoston E. Eiben
  2. University of the West of England, Bristol BS16 1QY, UK.

    • Jim Smith

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