{"references": ["A. E. Eiben and J. E. Smith: Introduction to Evolutionary Computing, Springer, Amsterdam, 2003.", "C. McKay: \"Automatic music classification with jMIR\". Ph.D. Thesis. McGill University, Canada, 2010.", "C. McKay, R. Fiebrink, D. McEnnis, B. Li, and I. Fujinaga: \"ACE: A framework for optimizing music classification.\" Proc. of the International Conference on Music Information Retrieval. 42\u20139, 2005.", "F. Pachet and A. Zils: \"Evolving Automatically High-Level Music Descriptors from Acoustic Signals.\" Proc. of the International Symp. on Computer Music Modeling and Retrieval. Springer Verlag LNCS, 2771, 2003.", "F. Pachet and P. Roy: \"Exploring Billions of audio features.\" Proc. of the International Workshop on Content-Based Multimedia Indexing, pp. 227 - 235, 2007.", "I.T. Jolliffe: Principal Component Analysis, Springer, Nova York, 2002.", "N. Srebro, J. Rennie; T. Jaakkola: \"Maximum-Margin Matrix Factorization\", Proc. of the Conference on Neural Information Processing System, 2004.", "Y. Bengio, A. Courville, P. Vincent: \"Representation Learning: A Review and New Perspectives\". IEEE Trans. PAMI, special issue Learning Deep Architectures, 2013.", "http://www.daccord.com.br/"]}

Choosing good features is an essential part of machine learning. Recent techniques aim to automate this process. For instance, feature learning intends to learn the transformation of raw data into a useful representation to machine learning tasks. In automatic audio classification tasks, this is interesting since the audio, usually complex information, needs to be transformed into a computationally convenient input to process. Another technique tries to generate features by searching a feature space. Genetic algorithms, for instance, have being used to generate audio features by combining or modifying them. We find this approach particularly interesting and, despite the undeniable advances of feature learning approaches, we wanted to take a step forward in the use of genetic algorithms to find audio features, combining them with more conventional methods, like PCA, and inserting search control mechanisms, such as constraints over a confusion matrix. This work presents the results obtained on particular audio classification problems.