Shortcomings with Tree-Structured Edge Encodings for
Neural Networks
Genetic and Evolutionary Computation -- GECCO-2004,
Part II, volume 3103 of Lecture Notes in Computer Science, page 495--506. Seattle, WA, USA, Springer-Verlag, (26-30 June 2004)DOI: doi:10.1007/b98645
Abstract
In evolutionary algorithms a common method for
encoding neural networks is to use a tree-structured
assembly procedure for constructing them. Since node
operators have difficulties in specifying edge weights
and these operators are execution-order dependent, an
alternative is to use edge operators. Here we identify
three problems with edge operators: in the
initialisation phase most randomly created genotypes
produce an incorrect number of inputs and outputs;
variation operators can easily change the number of
input/output (I/O) units; and units have a connectivity
bias based on their order of creation. Instead of
creating I/O nodes as part of the construction process
we propose using parameterised operators to connect to
pre-existing I/O units. Results from experiments show
that these parameterized operators greatly improve the
probability of creating and maintaining networks with
the correct number of I/O units, remove the
connectivity bias with I/O units and produce better
controllers for a goal-scoring task.
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