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Evolutionary Optimization of Case-Based Forecasting Algorithms in Chaotic Environments
2021
Symmetry
The problem of dynamic adaptation of prediction algorithms in chaotic environments based on identification of the situations-analogs in the database of retrospective observations is considered. Under conditions of symmetrical and unsymmetrical chaotic dynamics, traditional computational schemes of precedent prediction turn out to be ineffective. In this regard, a dynamic adaptation of precedent analysis algorithms based on the method of evolutionary modeling is proposed. Implementation of the
doi:10.3390/sym13020301
fatcat:7jbcfhe7enbg5nxzlbfoj24iye