Many neuroevolution methods evolve fixed-topology
networks.
Some methods evolve topologies in addition to
weights, but these usually have a bound on the complexity of
networks that can be evolved and begin evolution with random topologies.
This project is based on a
neuroevolution method called
NeuroEvolution of Augmenting Topologies (NEAT)
that can evolve networks of unbounded complexity from a minimal
starting point.
The initial stage of research aims to
demonstrate that topology can be used to increase
the efficiency of search if it
minimizes the dimensionality of the weight space.
We performed several pole balancing experiments
that demonstrate that evolving topology using NEAT indeed
provides an advantage.
However, the research has a broader goal of showing
that evolving topologies is necessary to achieve 3
major goals of neuroevolution: (1) Continual coevolution: Successful competitive
coevolution can use the evolution of topologies to
continuously elaborate strategies. (2) Evolution of Adaptive Networks:
The evolution
of topologies allows neuroevolution to evolve
adaptive networks with plastic synapses by designating
which connections should be adaptive and in what ways.
(3) Combining Expert Networks: Separate expert neural networks can be fused
through the evolution of connecting neurons between them.
Because we want to show that
growing structure is necessary to achieve these
goals, it is important that an efficient and principled method for
evolving topologies is available for experimentation.
NEAT provides just such an experimental platform.
NEAT is also an important contribution to
GAs because it shows how it is possible for evolution
to both optimize
and complexify solutions simultaneously,
making it possible to evolve
increasingly
complex solutions over time, thereby strengthening the
analogy with
biological evolution.