1 research outputs found
Evolutionary Strategies for Novelty-Based Online Neuroevolution in Swarm Robotics
Neuroevolution in robot controllers
throughobjective-based genetic and evolutionary
algorithms is a wellknownmethodology for studying
the dynamics of evolution inswarms of simple
robots. A robot within a swarm is able toevolve
the simple neural network embedded as its
controllerby also taking into account how other
robots are performingthe task at hand. In online
scenarios, this is obtained throughinter-robot
communications of the best performing genomes
(i.e.representation of the weights of their
embedded neural network).While many experiments
from previous work have shown thesoundness of
this approach, we aim to extend this
methodologyusing a novelty-based metric, so to be
able to analyze differentgenome exchange
strategies within a simulated swarm of robotsin
deceptive tasks or scenarios in which it is
difficult to modela proper objective function to
drive evolution. In particular, wewant to study
how different information sharing
approachesaffect the evolution. To do so we
developed and tested threedifferent ways to
exchange genomes and information betweenrobots
using novelty driven evolution and we compared
themusing a recent variation of the mEDEA
(minimal EnvironmentdrivenDistributed
Evolutionary Algorithm) algorithm. As
thedeceptiveness and the complexity of the task
increases, ourproposed novelty-driven strategies
display better performancein foraging scenarios