Research Output

Improving survivability in environment-driven distributed evolutionary algorithms through explicit relative fitness and fitness proportionate communication.

  Ensuring the integrity of a robot swarm in terms of maintaining
a stable population of functioning robots over long
periods of time is a mandatory prerequisite for building more
complex systems that achieve user-defined tasks. mEDEA
is an environment-driven evolutionary algorithm that provides
promising results using an implicit fitness function
combined with a random genome selection operator. Motivated
by the need to sustain a large population with sufficient
spare energy to carry out user-defined tasks in the future,
we develop an explicit fitness metric providing a measure
of fitness that is relative to surrounding robots and
examine two methods by which it can influence spread of
genomes. Experimental results in simulation find that use of
the fitness-function provides significant improvements over
the original algorithm; in particular, a method that influences
the frequency and range of broadcasting when combined
with random selection has the potential to conserve
energy whilst maintaining performance, a critical factor for
physical robots.

  • Publication Status:

    Published

  • DOI:

    10.1145/2739480.2754688

  • Library of Congress:

    QA75 Electronic computers. Computer science

  • Dewey Decimal Classification:

    006.3 Artificial intelligence

Citation

Hart, E., Steyven, A. & Paechter, B. (2014). Improving survivability in environment-driven distributed evolutionary algorithms through explicit relative fitness and fitness proportionate communication. In Proceedings of the 2015 on Genetic and Evolutionary Computation Conference - GECCO '15, 169-176. doi:10.1145/2739480.2754688. ISBN 978-1-4503-3472-3

Authors

Keywords

Evolutionary Robotics; Environment-driven; On-line Evolution;

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