Constraints-Preserving Genetic Algorithm for Learning Fuzzy Measures with an Application to Ontology Matching

M. Al Boni, Anderson, D., & King, R. (2013). Constraints-Preserving Genetic Algorithm for Learning Fuzzy Measures with an Application to Ontology Matching. Advance Trends in Soft Computing. San Antonio, TX, USA: Springer International Publishing. 312, 93-103. DOI:10.1007/978-3-319-03674-8_9.

Both the fuzzy measure and integral have been widely studied for multi-source information fusion. A number of researchers have proposed optimization techniques to learn a fuzzy measure from training data. In part, this task is difficult as the fuzzy measure can have a large number of free parameters (2^N-2 for N sources) and it has many (monotonicity) constraints. In this paper, a new genetic algorithm approach to constraint preserving optimization of the fuzzy measure is present for the task of learning and fusing different ontology matching results. Preliminary results are presented to show the stability of the leaning algorithm and its effectiveness compared to existing approaches.