Abstract
Today researchers need to solve vague defined problems working with huge data sets describing signals close to chaotic ones. Common feature of such signals is missing algebraic model explaining their nature. Genetic Algorithms and Evolutionary Strategies are suitable to optimize such models and Genetic Programming Algorithms to develop them. Hierarchical GPA-ES algorithm presented herein is used to build compact models of difficult signals including signals representing deterministic chaos. Efficiency of GPA-ES is presented in the paper. Specific group of non-linearly composed functions similar to real biomedical signals is studied in the paper. On the base of these prerequisites, models applicable in complex biomedical signals like EEG modeling are formed and studied within the contribution.
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Brandejsky, T. (2013). The Use of Local Models Optimized by Genetic Programming Algorithms in Biomedical-Signal Analysis. In: Zelinka, I., Snášel, V., Abraham, A. (eds) Handbook of Optimization. Intelligent Systems Reference Library, vol 38. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-30504-7_28
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DOI: https://doi.org/10.1007/978-3-642-30504-7_28
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