MAP-Elites with CPPNs, DSP graphs, and a deep classifier produces diverse synthetic sounds across durations and musical/non-musical contexts.
In: Proceedings of the Genetic and Evolutionary Computation Conference Companion
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PIAS in black-box optimization remains beneficial versus the single best algorithm for most tested cases even with 25% budget spent on features, and feature computation explains about 20% of the average performance gap to the virtual best solver.
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Quality-Diversity Search in Sound Generation: Investigating Innovation Engines for Audio Exploration
MAP-Elites with CPPNs, DSP graphs, and a deep classifier produces diverse synthetic sounds across durations and musical/non-musical contexts.
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On the Influence of the Feature Computation Budget on Per-Instance Algorithm Selection for Black-Box Optimization
PIAS in black-box optimization remains beneficial versus the single best algorithm for most tested cases even with 25% budget spent on features, and feature computation explains about 20% of the average performance gap to the virtual best solver.