Support Vector Machines (SVMs) have achieved very good performance on different learning problems. However, the success of SVMs depends on the adequate choice of a number of parameters, including for instance the kernel and the regularization parameters. In the current work, we propose the combination of Meta-Learning and search techniques to the problem of SVM parameter selection. Given an input problem, Meta-Learning is used to recommend SVM parameters based on well-succeeded parameters adopted in previous similar problems. The parameters returned by Meta-Learning are then used as initial search points to a search technique which will perform a further exploration of the parameter space. In this combination, we envisioned that the initial solutions provided by Meta-Learning are located in good regions in the search space (i.e. they are closer to the optimum solutions). Hence, the search technique would need to evaluate a lower number of candidate search points in order to find an adequate solution. In our work, we implemented a prototype in which Particle Swarm Optimization (PSO) was used to select the values of two SVM parameters for regression problems. In the performed experiments, the proposed solution was compared to a PSO with random initialization, obtaining better average results on a set of 40 regression problems.