Adaptive global optimization with local search
Adaptive global optimization with local search
One-Dimensional Digital Signal Processing
One-Dimensional Digital Signal Processing
DE/EDA: a new evolutionary algorithm for global optimization
Information Sciences—Informatics and Computer Science: An International Journal
Completely Derandomized Self-Adaptation in Evolution Strategies
Evolutionary Computation
GA-EDA: hybrid evolutionary algorithm using genetic and estimation of distribution algorithms
IEA/AIE'2004 Proceedings of the 17th international conference on Innovations in applied artificial intelligence
Population structure and particle swarm performance
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
Population structure and particle swarm performance
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
Special issue on emerging trends in soft computing: memetic algorithms
Soft Computing - A Fusion of Foundations, Methodologies and Applications - Special Issue on Emerging Trends in Soft Computing - Memetic Algorithms; Guest Editors: Yew-Soon Ong, Meng-Hiot Lim, Ferrante Neri, Hisao Ishibuchi
Self-adaptive multimethod search for global optimization in real-parameter spaces
IEEE Transactions on Evolutionary Computation
Differential evolution algorithm with strategy adaptation for global numerical optimization
IEEE Transactions on Evolutionary Computation
A probabilistic memetic framework
IEEE Transactions on Evolutionary Computation
Artificial immune algorithm for IIR filter design
Engineering Applications of Artificial Intelligence
Information Sciences: an International Journal
Research frontier: memetic computation-past, present & future
IEEE Computational Intelligence Magazine
Digital IIR filter design using multi-objective optimization evolutionary algorithm
Applied Soft Computing
Self-adaptive learning based particle swarm optimization
Information Sciences: an International Journal
A WISE method for designing IIR filters
IEEE Transactions on Signal Processing
Automatic design of frequency sampling filters by hybrid geneticalgorithm techniques
IEEE Transactions on Signal Processing
Complexity reduction of digital filters using shift inclusive differential coefficients
IEEE Transactions on Signal Processing
Meta-Lamarckian learning in memetic algorithms
IEEE Transactions on Evolutionary Computation
Systematic integration of parameterized local search into evolutionary algorithms
IEEE Transactions on Evolutionary Computation
A tutorial for competent memetic algorithms: model, taxonomy, and design issues
IEEE Transactions on Evolutionary Computation
Comprehensive learning particle swarm optimizer for global optimization of multimodal functions
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Evolutionary Computation
Accelerating Differential Evolution Using an Adaptive Local Search
IEEE Transactions on Evolutionary Computation
Classification of adaptive memetic algorithms: a comparative study
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A Fast Adaptive Memetic Algorithm for Online and Offline Control Design of PMSM Drives
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A Memetic Algorithm for VLSI Floorplanning
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Information Sciences: an International Journal
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The research on optimal design of infinite-impulse response (IIR) filters based on optimization techniques has gained much attention in recent years. However, due to the limited performance of the applied optimization techniques, the orders of the filters, which can be obtained, are very low in the previous research. Memetic algorithms (MAs) are widely recognized to have better convergence capability than their conventional counterparts. However, the universality of the MAs, e.g. the ability of solving diverse kinds of digital IIR filter designs, is still limited. In this paper, we design a Two-Stage ensemble Memetic Algorithm (TSMA) framework to more appropriately synthesize the strengths of the evolutionary global search and local search techniques. In the first optimization stage, a competition is held among the candidate local search techniques. Its major idea is to choose the best local search technique and to obtain good initial state. Inheriting the good information of the first stage, the second optimization stage is to implement effective adaptive MA to pursue high-quality solution. The experimental studies presented in this paper contain three aspects: (1) the benefits of the TSMA framework are experimentally investigated by comparing TSMA with its sub-optimizers and recent effective evolutionary algorithms (EAs) on 26 test functions; then (2) TSMA is compared with 4 MAs on the CEC05 functions to comprehensively show the advantages of TSMA; and (3) the TSMA and 6 state-of-the-art algorithms are applied to design high-order digital infinite-impulse response (IIR) filters. The experimental results definitely demonstrate the excellent effectiveness, efficiency and reliability of TSMA on both function optimization and digital IIR filter design tasks.