Cartesian Genetic Programming Parameterization in the Context of Audio Synthesis

This letter presents an evaluation of the effects of elitism, recurrence probability, and prior knowledge on the fitness achieved by Cartesian Genetic Programming (CGP) in the context of DSP audio synthesis. Prior knowledge was introduced using a probabilistic learning method where the distribution...

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Vydáno v:IEEE Signal Processing Letters Ročník 30; s. 1077 - 1081
Hlavní autoři: Ly, Edward, Villegas, Julian
Médium: Journal Article
Jazyk:angličtina
japonština
Vydáno: New York IEEE 2023
Institute of Electrical and Electronics Engineers (IEEE)
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Additives
Audio synthesis
Cartesian coordinates
Cartesian genetic programming
Computer languages
Context
digital signal processing
evolutionary algorithms
Feedback loop
Genetic algorithms
Genetic programming
Nodes
Oscillators
Parameterization
Statistical analysis
Synthesis
Synthesizers
Wheels
ISSN:1070-9908, 1558-2361
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Shrnutí:This letter presents an evaluation of the effects of elitism, recurrence probability, and prior knowledge on the fitness achieved by Cartesian Genetic Programming (CGP) in the context of DSP audio synthesis. Prior knowledge was introduced using a probabilistic learning method where the distribution of nodes in the expected solutions was used to generate and mutate new individuals. Best results were obtained with traditional elitist selection, no recurrence, and when prior knowledge was used for node initialization and mutation. These results suggest that the apparent benefits of recurrence in CGP are context-dependent, and that selecting nodes from a uniform distribution is not always optimal.
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ISSN:1070-9908
1558-2361
DOI:10.1109/LSP.2023.3304198