An enhanced memetic differential evolution in filter design for defect detection in paper production.
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.
Tirronen
Ville
V
Department of Mathematical Information Technology, Agora, University of Jyväskylä, P.O. Box 35 (Agora), FI-40014 University of Jyväskylä, Finland. aleator@jyu.fi
Neri
Ferrante
F
Kärkkäinen
Tommi
T
Majava
Kirsi
K
Rossi
Tuomo
T
D000465:Algorithms; D001185:Artificial Intelligence; D003198:Computer Simulation; D001330:Electronic Data Processing / Q000379:methods*; D006801:Humans; D007090:Image Interpretation, Computer-Assisted; D007221:Industry; D015233:Models, Statistical; D008962:Models, Theoretical; D010209:Paper; D011336:Probability; D011786:Quality Control; D012984:Software; D013269:Stochastic Processes