Abstract
Nonlinear multimodal filtering problems are usually addressed via Monte Carlo algorithms. These algorithms involve sampling procedures that are similar to proportional selection in genetic algorithms, and that are prone to failure due to genetic drift. This work investigates the feasibility and the relevance of niching strategies in this context. Sharing methods are evaluated experimentally, and prove to be efficient in such issues.
Current address: TIMC, Faculté de Médecine, Domaine de la Merci, 38706 La Tronche Cedex.
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Bienvenüe, A., Joannides, M., Bérard, J., Fontenas, É., François, O. (2002). Niching in Monte Carlo Filtering Algorithms. In: Collet, P., Fonlupt, C., Hao, JK., Lutton, E., Schoenauer, M. (eds) Artificial Evolution. EA 2001. Lecture Notes in Computer Science, vol 2310. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-46033-0_2
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DOI: https://doi.org/10.1007/3-540-46033-0_2
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