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

EVOLUTIONARY COMPUTATION 2008年第4期16卷 529-555页

作者： Tirronen, Ville Neri, Ferrante Karkkainen, Tommi Majava, Kirsi Rossi, Tuomo Univ Jyvaskyla Dept Math Informat Technol FI-40014 Agora Finland

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.

关键词： Memetic algorithms differential evolution multimeme algorithms digital filter design FIR filter paper production edge detection

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Memetic algorithms: The polynomial local search complexity theory perspective

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Journal of Mathematical Modelling and algorithms 2008年第1期7卷 3-24页

作者： Krasnogor, Natalio Smith, Jim Automated Scheduling Optimisation and Planning Research Group School of Computer Science and IT University of Nottingham Nottingham United Kingdom Faculty of Computing Engineering and Mathematical Sciences University of the West of England Bristol United Kingdom

In previous work (Krasnogor, http://***/~nxk/*** . In: Studies on the Theory and Design Space of Memetic algorithms. Ph.D. thesis, University of the West of England, Bristol, UK, 2002;Krasnogor and Smith, IEEE Trans Evol algorithms 9(6):474-488, 2005) we develop a syntax-only classification of evolutionary algorithms, in particular so-called memetic algorithms (MAs). When "syntactic sugar" is added to our model, we are able to investigate the polynomial local search (PLS) complexity of memetic algorithms. In this paper we show the PLS-completeness of whole classes of problems that occur when memetic algorithms are applied to the travelling salesman problem using a range of mutation, crossover and local search operators. Our PLS-completeness results shed light on the worst case behaviour that can be expected of a memetic algorithm under these circumstances. Moreover, we point out in this paper that memetic algorithms for graph partitioning and maximum network flow (both with important practical applications) also give rise to PLS-complete problems. © 2007 Springer Science + Business Media B.V.

关键词： Evolutionary hybrid algorithms Genetic local search hybrids Graph partitioning Hyper-heuristics Memetic algorithms Meta-Lamarckian algorithms multimeme algorithms Polynomial local search Travelling salesman problem

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Scale factor local search in differential evolution

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Memetic Computing 2009年第2期1卷 153-171页

作者： Neri, Ferrante Tirronen, Ville Department of Mathematical Information Technology Agora University of Jyväskylä Jyväskylä 40014 Finland

This paper proposes the scale factor local search differential evolution (SFLSDE). The SFLSDE is a differential evolution (DE) based memetic algorithm which employs, within a self-adaptive scheme, two local search algorithms. These local search algorithms aim at detecting a value of the scale factor corresponding to an offspring with a high performance, while the generation is executed. The local search algorithms thus assist in the global search and generate offspring with high performance which are subsequently supposed to promote the generation of enhanced solutions within the evolutionary framework. Despite its simplicity, the proposed algorithm seems to have very good performance on various test problems. Numerical results are shown in order to justify the use of a double local search instead of a single search. In addition, the SFLSDE has been compared with a standard DE and three other modern DE based metaheuristic for a large and varied set of test problems. Numerical results are given for relatively low and high dimensional cases. A statistical analysis of the optimization results has been included in order to compare the results in terms of final solution detected and convergence speed. The efficiency of the proposed algorithm seems to be very high especially for large scale problems and complex fitness landscapes. © 2009 Springer-Verlag.

关键词： Adaptive memetic algorithms Differential evolution Golden section search Large scale optimization multimeme algorithms

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