In this work we present GOAL (Genetics for Ontology Align- ments) a new approach to compute the optimal ontology alignment func- tion for a given ontology input set. Although this problem could be solved by an exhaustive search when the number of similarity measures is low, our method,is expected to scale better for a high number,of measures. Our approach is a genetic algorithm which is able to work with several goals: maximizing the alignment precision, maximizing the alignment re- call, maximizing the f-measure or reducing the number of false positives. Moreover, we test it here by combining some cutting-edge similarity mea- sures over a standard benchmark, and the results obtained show several advantages in relation to other techniques. Key words: ontology alignment; genetic algorithms; semantic integra-
