Evolutionary techniques for the synthesis of 2-D FIR filters

Boudjelaba, Kamal; Chikouche, Djamel; Ros, Frédéric

doi:10.1109/ssp.2011.5967771

Cited by 12 publications

(9 citation statements)

References 19 publications

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“…if D fitness (ch current ,ch inspected ) ≥ f 1 continue else if D gene (ch current ,ch inspected ) ≥ g 1 continue else discard ch inspected ⎧ ⎨ ⎩ (19) ch current (ch inspected ) stands for chromosome current as Δ fitness (resp.Δ gene ) stands for distance in the fitness space and f ε (resp. g ε ) is a predefined threshold.…”

Section: Selection Scheme Refinementmentioning

confidence: 99%

“…Some evolutionary filter design methods have been implemented [2,8,13,16,17] for about 10 years with promising results. We have already investigated GA for designing one-dimensional (1D) and 2D FIR filters [18][19][20] by developing dedicated GAs for the synthesis of 1D and 2D FIR filters. We demonstrated that by adding some flexibility in classical GA versions, ripples can be significantly reduced compared to the traditional and genetic approaches [13].…”

Section: Introductionmentioning

confidence: 99%

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Adaptive genetic algorithm‐based approach to improve the synthesis of two‐dimensional finite impulse response filters

Boudjelaba

Ros

Chikouche

2014

IET signal process.

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International audienceThe design of finite impulse response (FIR) filters can be formulated as a non-linear optimization problem reputed to be difficult for conventional approaches. The constraints are high and a large number of parameters have to be estimated, especially when dealing with 2-D FIR filters. In order to improve the performance of conventional approaches, we explore several stochastic methodologies capable of handling large spaces. We specifically propose a new genetic algorithm in which some innovative concepts are introduced to improve the convergence and make its use easier for practitioners. The algorithm is globally improved by adapting the mutation and crossover and selection operators with the genetic advances. A dynamic ranking selection scheme is introduced to limit the promotion of extraordinary chromosomes. A refreshing mechanism is investigated to manage the trade-off between diversity and elitism. The key point of our approach stems from the capacity of the genetic algorithm (GA) to adapt the genetic operators during the genetic life while remaining simple and easy to implement. Most of parameters and operators are changed by the GA itself. From an initial calibration, the GA performs the design problem while calibrating and repeatedly re-calibrating itself for solving it. We demonstrate on various cases of filter design a significant improvement in performance

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Section: Selection Scheme Refinementmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Adaptive genetic algorithm‐based approach to improve the synthesis of two‐dimensional finite impulse response filters

Boudjelaba

Ros

Chikouche

2014

IET signal process.

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“…Therefore, several theorems have been proposed by Shanks et al (1972), Huang (1972, Strintzis (1977), De Carlo et al (1977, Jury (1978), Bose (1982). In addition, Lu and Lee (1985) developed a Lyapunov stability based approach for 2-D systems and Mastorakis et al (2003) described a design method with respect to constrained optimization for the design of the 2-D recursive digital filter.…”

Section: The Stability Of 2-d Digital Filtersmentioning

confidence: 99%

The design approaches of two-dimensional digital filters based on metaheuristic optimization algorithms: a review of the literature

Koçkanat

Karaboğa

2015

Artif Intell Rev

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Recently, the design of the two-dimensional digital filter has become a subject of interest in the field of two-dimensional signal processing. The two-dimensional digital filter has been applied in many important areas such as image processing, television systems and seismic signal processing. In digital filter design, there are several indispensable aims such as stability, reduced computational complexity and computational time. Thus, researchers and practitioners have investigated various advanced methods based on metaheuristic optimization algorithms for the design of the two-dimensional digital filter. Metaheuristic optimization algorithms have been applied to solve different complicated problems in various fields and they have also been successfully used in digital filter design. This paper presents a review of the design approaches of two-dimensional digital filters based on metaheuristic optimization algorithms such as the genetic algorithm, differential evolution and particle swarm optimization. By comparing the proposed design approaches based on metaheuristic optimization algorithms, it is observed that the genetic algorithm is the most preferred algorithm and emerging novel algorithms using metaheuristic optimization algorithms have better performance in terms of computational complexity and computational time. It is hoped that this review will be helpful for researchers and practitioners studying the design of twodimensional digital filters.

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“…It combines filter design knowhow and expertise in the application of various GAs for different optimization problems. A preliminary version of the algorithm has already been presented in Boudjelaba [30,31]. This version has been improved by a better selection scheme and simplification of the parameter management.…”

Section: Advanced Pso and Filter Designmentioning

confidence: 99%

Potential of Particle Swarm Optimization and Genetic Algorithms for FIR Filter Design

Boudjelaba

Ros

Chikouche

2014

Circuits Syst Signal Process

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Evolutionary techniques for the synthesis of 2-D FIR filters

Cited by 12 publications

References 19 publications

Adaptive genetic algorithm‐based approach to improve the synthesis of two‐dimensional finite impulse response filters

Adaptive genetic algorithm‐based approach to improve the synthesis of two‐dimensional finite impulse response filters

The design approaches of two-dimensional digital filters based on metaheuristic optimization algorithms: a review of the literature

Potential of Particle Swarm Optimization and Genetic Algorithms for FIR Filter Design

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