Multiplier-less FIR filter design using a genetic algorithm

Wade, Graham; Roberts, A. P.; Williams, Guy

doi:10.1049/ip-vis:19941185

Cited by 50 publications

(19 citation statements)

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“…In particular, structures of multiplierless filters were sought by many authors (Wade et al 1994;Hounsell et al 2004;Erba et al 2001;Gwaltney and Dutton 2005). Regardless of filter representation, the fitness function is usually constructed in the frequency domain.…”

Section: Digital Filter Evolutionmentioning

confidence: 99%

Evolvable Hardware

Sekanina

2012

Handbook of Natural Computing

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Section: Digital Filter Evolutionmentioning

confidence: 99%

Evolvable Hardware

Sekanina

2012

Handbook of Natural Computing

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“…In the literature, there are several works about the application of genetic algorithms (GAs) to the digital FIR filter design [5], [13], [14], [22], [23]. In these works, it is emphasized that filters designed by a GA have the potential of obtaining near global optimum solutions.…”

Section: Introductionmentioning

confidence: 99%

Design of Digital FIR Filters Using Differential Evolution Algorithm

Karaboğa

Cetinkaya

2006

Circuits Syst Signal Process

139

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The differential evolution (DE) algorithm is a new heuristic approach with three main advantages: it finds the true global minimum of a multimodal search space regardless of the initial parameter values, it has fast convergence, and it uses only a few control parameters. The DE algorithm, which has been proposed particularly for numeric optimization problems, is a population-based algorithm like the genetic algorithms and uses similar operators: crossover, mutation, and selection. In this work, the DE algorithm has been applied to the design of digital finite impulse response filters, and its performance has been compared to that of the genetic algorithm and least squares method.

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“…The better the basis filter attenuates unwanted components and the better the prediction capabilities of the basis filter are, the easier it is for the MGP filter to adapt to the changing characteristics of the input signal. Digital filters have been designed before using genetic algorithms, for example in [8] [9], but MGP-FIRs have never been designed using adaptive genetic algorithms. Thus, the speedup in convergence of the GA in this application using adaptive parameters was yet to be studied.…”

Section: Introductionmentioning

confidence: 99%

Designing Multiplicative General Parameter Filters Using Adaptive Genetic Algorithms

Martikainen¹,

Ovaska²

2004

Genetic and Evolutionary Computation – GECCO 2004

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Abstract. Multiplicative general parameter (MGP) approach to finite impulse response (FIR) filtering introduces a novel way to realize cost effective adaptive filters in compact very large scale integrated circuit (VLSI) implementations used for example in mobile devices. MGP-filter structure comprises of additions and only a small number of multiplications, thus making the structure very simple. Only a couple of papers have been published on this recent innovation and, moreover, MGP-filters have never been designed using adaptive genetic algorithms (GA). The notion suggesting the use of adaptive parameters is that optimal parameters of an algorithm may change during the optimization process, and thus it is difficult to define parameters beforehand that would produce competitive solutions. In this paper, we present results of designing MGP-FIR basis filters using different types of adaptive genetic algorithms, and compare the results to the ones obtained using a simple GA.

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Multiplier-less FIR filter design using a genetic algorithm

Cited by 50 publications

References 0 publications

Evolvable Hardware

Evolvable Hardware

Design of Digital FIR Filters Using Differential Evolution Algorithm

Designing Multiplicative General Parameter Filters Using Adaptive Genetic Algorithms

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