An Enhanced Memetic Differential Evolution in Filter Design for Defect Detection in Paper Production

Abstract

This article proposes an Enhanced Memetic Differential Evolution (EMDE) for designing digital filters which aim at detecting defects of the paper produced during an industrial process. Defect detection is handled by means of two Gabor filters and their design is performed by the EMDE. The EMDE is a novel adaptive evolutionary algorithm which combines the powerful explorative features of Differential Evolution with the exploitative features of three local search algorithms employing different pivot rules and neighborhood generating functions. These local search algorithms are the Hooke Jeeves Algorithm, a Stochastic Local Search, and Simulated Annealing. The local search algorithms are adaptively coordinated by means of a control parameter that measures fitness distribution among individuals of the population and a novel probabilistic scheme. Numerical results confirm that Differential Evolution is an efficient evolutionary framework for the image processing problem under investigation and show that the EMDE performs well. As a matter of fact, the application of the EMDE leads to a design of an efficiently tailored filter. A comparison with various popular metaheuristics proves the effectiveness of the EMDE in terms of convergence speed, stagnation prevention, and capability in detecting solutions having high performance.

Differential evolution schemes for speech segmentation: A comparative study

“…1. The thresholds are set by weighting the long time information of these three features as shown in (13), (14) and (15). The average energy, ZCCR and unit delay autocorrelation (short time information) of the speech segment to be classified are compared with the thresholds for decision making.…”

“…A popular modification of the original DE scheme consists of hybridisation with local search, often coordinated by adaptive rules. By means of this hybridisation, modern implementations based on DE but tailored to specific filter design problems for defect detection in paper production are given in [15], [16], and [17]. The most recent studies on this topic use improved/modified versions of DE for classical filter design problems, such as the design of Finite Impulse Response (FIR) and Infinite Impulse Response (IIR) filters, see [18], or modified DE based schemes to address advanced engineering problems such as the design of two-channel quadrature mirror filters with linear phase characteristics, see [19].…”

Automatically Modeling Hybrid Evolutionary Algorithms from Past Executions

“…In both strategies, there is a learning phase in which the performance of each local search is stored and used in later generations in order to select the local search to apply. In [1,10] several local searches are combined with a metaheuristic algorithm using also an adaptive scheme. The application of each algorithm is based on a population diversity measure which varies among the studies.…”

Evolutionary-based Image Segmentation Methods

“…A lot of studies have demonstrated that MAs converge to high-quality solutions more efficiently than their conventional counterparts in many realworld applications [17][18][19][20][21][22][23][24][25][26][27][28][29][30][31][32]. MAs have also been applied in the image processing field [33][34][35][36][37][38]. For example, Fernandez, Garana and Cabello [33] proposed a MA-based method for the correction of illumination inhomogeneities in images.…”

“…Batenburg [34] designed an EA with hillclimb operator for finding a binary image that satisfies prescribed horizontal and vertical projections. Tirronen, Neri et al [35] studied the defect detection in paper production by means of image-processing techniques based on memetic differential evolution frameworks. Gesù, Bosco et al [36,37] introduced a new memetic approach for the reconstruction of binary images.…”