An enhanced memetic differential evolution in filter design for defect detection in paper production.

Date

2008

Advisors

Journal Title

Journal ISSN

ISSN

1063-6560

Volume Title

Publisher

MIT Press

Type

Article

Peer reviewed

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.

Description

Keywords

memetic algorithms, differential evolution, multimeme algorithms, digital filter design, FIR filter, paper production, edge detection

Citation

Tirronen, V., Neri, F., Kärkkäinen, T., Majava, K. and Rossi, T. (2008) An Enhanced Memetic Differential Evolution in Filter Design for Defect Detection in Paper Production. Evolutionary Computation Journal, 16 (4), pp. 529-555

Rights

Research Institute