Title :
Accurate Resolution of Signals Using Integer-Coded Genetic Algorithms
Author_Institution :
Mentor Graphics Inc., Cairo
Abstract :
In this paper, an integer-coded genetic algorithm (GA) is proposed to obtain accurate and parsimonious sinusoidal representation of signals. The method suggested addresses the problem of finding the most significant sinusoidal frequencies which lead to the smallest modeling error. A variable length GA chromosome which encodes the number of possible component frequencies and their actual locations is used. The weight of each component is calculated using a least squares identification method. A set of evolutionary operators are selected to suit the integer representation of the genes in each chromosome. The proposed algorithm produced excellent results in modeling noise-free and noisy time series of short data records.
Keywords :
genetic algorithms; least mean squares methods; signal representation; signal resolution; GA chromosome; evolutionary operators; integer-coded genetic algorithms; least squares identification method; signals resolution; time series analysis; Biological cells; Biological system modeling; Circuit noise; Difference equations; Frequency estimation; Genetic algorithms; Least squares methods; Signal processing algorithms; Signal resolution; Time series analysis;
Conference_Titel :
Evolutionary Computation, 2006. CEC 2006. IEEE Congress on
Conference_Location :
Vancouver, BC
Print_ISBN :
0-7803-9487-9
DOI :
10.1109/CEC.2006.1688672