o;?1. Automatic Synthesizer Preset Generation with PresetGen. 1. The name, complete physical mailing address, e-mail address, and phone number of EACH author of EACH paper(s); KD1vanC' Tatar: ktatar@sfu.ca; 1 778 858 6073 Matthieu Macret: matthieu.macret@gmail.com; 1 778229 1058 Philippe Pasquier: pasquier@sfu.ca; 1 778 989 1240 Address: SFU Surrey Campus; 250-13450 102nd Avenue, Surrey, B.C., Canada V3T 0A3 1. KD1vanC' Tatar: ktatar@sfu.ca 2. The abstract of the paper: We refer the task of finding preset(s) (i.e. set(s) of synthesizer parameters) that approximates a target sound best, as the preset generation problem. PresetGen addresses this problem regarding the real world synthesizer, OP-1. The OP-1 consists of several synthesis blocks, and it is not fully deterministic. We propose and evaluate a solution to preset generation using a multi-objective Non-dominated Sorting-Genetic-Algorithm-II. PresetGen handles the full problem complexity and returns a small set of presets that approximate the target sound best by covering the Pareto front of this multi-objective optimization problem. Moreover, we present an empirical evaluation experiment that compares the performance of three human sound designers to that of PresetGen. The results show that PresetGen is human-competitive. 1. This work satisfies the criteria F, G, and H. 2. This work focuses on the problem of synthesizer preset generation with OP-1. We define this problem as given a target sound, finding a synthesizer parameter set (i.e. preset) approximates give target sound best. We show that the search space of OP-1 synthesizer is 10^76, very large to be handled by a human. Moreover, we present an empirical evaluation experiment with three sound designers to show that PresetGen is human competitive on the task of synthesizer preset generation. 3. Tatar, K., Macret, M., & Pasquier, P. (2016). Automatic Synthesizer Preset Generation with PresetGen. Journal of New Music Research, 1b21. http://doi.org/10.1080/09298215.2016.1175481 4. Any prize money, if any, is to be divided equally among the co-authors. 5. We think that the synthesizer preset generation problem is unique and has many applications in music. Finding a synthesizer preset to match a target sound is tedious task since the search space of a synthesizer is huge (10^76 combinations in the case of OP-1). We hope that this work will contribute to both music industry and individuals producing music. 6. This work implements a multi-objective Genetic Algorithm, using Non-dominated Sorting-Genetic-Algorithm-II.