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

Hart, Emma; Steyven, Andreas; Paechter, Ben



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.

Presentation Conference Type Conference Paper (Published)
Conference Name Proceedings of the 2015 on Genetic and Evolutionary Computation Conference - GECCO '15
Start Date Jul 11, 2015
End Date Jul 15, 2015
Publication Date 2015
Deposit Date Oct 30, 2015
Publicly Available Date Dec 31, 2015
Peer Reviewed Peer Reviewed
Pages 169-176
Book Title Proceedings of the 2015 on Genetic and Evolutionary Computation Conference - GECCO '15
ISBN 9781450334723
Keywords Evolutionary Robotics; Environment-driven; On-line Evolution;
Public URL
Publisher URL
Contract Date Oct 30, 2015


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