  We introduce a novel evolutionary algorithm (EA) with a semantic
network-based representation. For enabling this, we establish new formulations
of EA variation operators, crossover and mutation, that we adapt to work on
semantic networks. The algorithm employs commonsense reasoning to ensure all
operations preserve the meaningfulness of the networks, using ConceptNet and
WordNet knowledge bases. The algorithm can be interpreted as a novel memetic
algorithm (MA), given that (1) individuals represent pieces of information that
undergo evolution, as in the original sense of memetics as it was introduced by
Dawkins; and (2) this is different from existing MA, where the word "memetic"
has been used as a synonym for local refinement after global optimization. For
evaluating the approach, we introduce an analogical similarity-based fitness
measure that is computed through structure mapping. This setup enables the
open-ended generation of networks analogous to a given base network.
