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

被引:102
作者
Tirronen, Ville [1 ]
Neri, Ferrante [1 ]
Karkkainen, Tommi [1 ]
Majava, Kirsi [1 ]
Rossi, Tuomo [1 ]
机构
[1] Univ Jyvaskyla, Dept Math Informat Technol, FI-40014 Agora, Finland
关键词
Memetic algorithms; differential evolution; multimeme algorithms; digital filter design; FIR filter; paper production; edge detection;
D O I
10.1162/evco.2008.16.4.529
中图分类号
TP18 [人工智能理论];
学科分类号
081104 [模式识别与智能系统]; 0812 [计算机科学与技术]; 0835 [软件工程]; 1405 [智能科学与技术];
摘要
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.
引用
收藏
页码:529 / 555
页数:27
相关论文
共 69 条
[1]
[Anonymous], 1996, EXPONENTIAL DISTRIBU
[2]
[Anonymous], 2000, P 2 ANN C GEN EV COM
[3]
[Anonymous], STUDIES COMPUTATIONA
[4]
Effective Memetic Algorithms for VLSI design = genetic algorithms plus local search plus multi-level clustering [J].
Areibi, S ;
Yang, Z .
EVOLUTIONARY COMPUTATION, 2004, 12 (03) :327-353
[5]
Bernsen J., PROC INT CONF PATT R, P1251
[6]
Genetic algorithm-based interactive segmentation of 3D medical images [J].
Cagnoni, S ;
Dobrzeniecki, AB ;
Poli, R ;
Yanch, JC .
IMAGE AND VISION COMPUTING, 1999, 17 (12) :881-895
[7]
A fast adaptive memetic algorithm for online and offline control design of PMSM drives [J].
Caponio, Andrea ;
Cascella, Giuseppe Leonardo ;
Neri, Ferrante ;
Salvatore, Nadia ;
Sumner, Mark .
IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS, 2007, 37 (01) :28-41
[9]
Fabric defect detection by Fourier analysis [J].
Chan, CH ;
Pang, GKH .
IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS, 2000, 36 (05) :1267-1276
[10]
Contextual-based Hopfield neural network for medical image edge detection [J].
Chang, Chuan-Yu .
OPTICAL ENGINEERING, 2006, 45 (03)