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Description: One of the most challenging problems in the semantic web field consists of computing the semantic similarity between different terms. The problem here is the lack of accurate domain-specific dictionaries, such as biomedical, financial or any other particular and dynamic field. In this article we propose a new approach which uses different existing semantic similarity methods to obtain precise results in the biomedical domain. Specifically, we have developed an evolutionary algorithm which uses information provided by different semantic similarity metrics. Our results have been validated against a variety of biomedical datasets and different collections of similarity functions. The proposed system provides very high quality results when compared against similarity ratings provided by human experts (in terms of Pearson correlation coefficient) surpassing the results of other relevant works previously published in the literature.