In this paper, we describe a novel application of evolutionary computation, namely for evolving ontologies. The general algorithm of evolutionary ontologies follow roughly the same guidelines as any other genetic algorithms. However, we introduced a new genetic operator, called repair, which is needed in order to make the offspring viable. Experiments for the generation of user centered automatically generated scenes demonstrate the performance of the proposed approach.

No abstract

Abstract-Recently introduced, evolutionary ontologies represent a new concept as a combination of genetic algorithms and ontologies. We have defined a new framework comprising a set of parameters required for any evolutionary algorithm, i.e. ontological space, representation of individuals, the main genetic operators such as selection, crossover, and mutation. Although a secondary operator, mutation proves its importance in creating and maintaining evolutionary ontologies diversity. Therefore, in this article, we widely debate the mutation topic in evolutionary ontologies, marking its usefulness in practice by experimental results. Also we introduce a new mutation operator, called relational mutation, concerning mutation of a relationship through its inverse.

No abstract

No abstract