Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control and Artificial Intelligence
Fragmentation and Frontier Evolution for Genetic Algorithms Optimization in Music Transcription
IBERAMIA '08 Proceedings of the 11th Ibero-American conference on AI: Advances in Artificial Intelligence
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Evolutionary algorithms and automatic transcription of music
Proceedings of the 14th annual conference companion on Genetic and evolutionary computation
Musical pitch estimation using a supervised single hidden layer feed-forward neural network
Expert Systems with Applications: An International Journal
Automatic music transcription: challenges and future directions
Journal of Intelligent Information Systems
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This paper presents the Gene Fragment Competition concept that can be used with Hybrid Genetic Algorithms specially in signal and image processing. Memetic Algorithms have shown great success in real-life problems by adding local search operators to improve the quality of the already achieved "good" solutions during the evolutionary process. Nevertheless these traditional local search operators don't perform well in highly demanding evaluation processes. This stresses the need for a new semi-local non-exhaustive method. Our proposed approach sits as a tradeoff between classical Genetic Algorithms and traditional Memetic Algorithms, performing a quasi-global/quasi-local search by means of gene fragment evaluation and selection. The applicability of this hybrid Genetic Algorithm to the signal processing problem of Polyphonic Music Transcription is shown. The results obtained show the feasibility of the approach.