Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control and Artificial Intelligence
Polar IFS+Parisian Genetic Programming=Efficient IFS Inverse Problem Solving
Genetic Programming and Evolvable Machines
Parisian camera placement for vision metrology
Pattern Recognition Letters - Special issue: Evolutionary computer vision and image understanding
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
Hybrid genetic algorithm based on gene fragment competition for polyphonic music transcription
Evo'08 Proceedings of the 2008 conference on Applications of evolutionary computing
Multipitch Analysis of Polyphonic Music and Speech Signals Using an Auditory Model
IEEE Transactions on Audio, Speech, and Language Processing
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Although traditional approaches in evolutionary computation encode each individual to represent the entire problem, the idea that an individual could be used to represent only part of it, is not new. Several different approaches exist that are based on decomposing the problem in smaller blocks/fragments, but the act of fragmentation can in some cases create unresolved issues, particularly on the fragments frontiers. This paper presents a method for optimizing some genetic algorithms applications, by fragment the problem in smaller ones, but keeping attention to frontier issues. While this paper focus on the application of the method to the music transcription problem, the proposed approach can be used on many other scenarios (signal processing, image analysis, etc.).