A semantic network-based evolutionary algorithm for computational creativity
This work addresses computational creativity for researchers in AI and evolutionary computation, but it is incremental as it builds on existing evolutionary and semantic network methods.
The authors tackled the problem of computational creativity by developing an evolutionary algorithm with a semantic network-based representation, using commonsense reasoning to preserve meaningfulness, and introduced an analogical similarity-based fitness measure for open-ended generation of analogous networks.
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.