Abstract
Differential Evolution (DE) algorithm is a new heuristic approach mainly having three advantages; finding the true global minimum of a multi modal search space regardless of the initial parameter values, fast convergence, and using a few control parameters. DE algorithm which has been proposed particulary for numeric optimization problems is a population based algorithm like genetic algorithms using the similar operators; crossover, mutation and selection. In this work, DE algorithm has been applied to the design of digital Finite Impulse Response filters and compared its performance to that of genetic algorithm.
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Karaboga, N., Cetinkaya, B. (2004). Performance Comparison of Genetic and Differential Evolution Algorithms for Digital FIR Filter Design. In: Yakhno, T. (eds) Advances in Information Systems. ADVIS 2004. Lecture Notes in Computer Science, vol 3261. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30198-1_49
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DOI: https://doi.org/10.1007/978-3-540-30198-1_49
Publisher Name: Springer, Berlin, Heidelberg
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