This thesis contains research on feature selection, in particular feature selection using evolutionary algorithms. Feature selection is motivated by increasing data-dimensionality and the need to construct simple induction models. A literature review of evolutionary feature selection is conducted. After that a abstract feature selection algorithm, capable of using many different wrappers, is constructed. The algorithm is configured using a low-dimensional dataset. Finally it is tested on a wide range of datasets, revealing both it's abilities and problems. The main contribution is the revelation that classifier accuracy is not a sufficient metric for feature selection on high-dimensional data. Preface This thesis is the intellectual product of Sigve Dreyer, at the artificial intelligence group at the department of computer and information science at NTNU. I would like to thank Anders Kofod-Petersen for his supervision and guidance. I also give credit to Bjoern Magnus Mathisen for providing a high performance testing environment.