Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Handbook of Evolutionary Computation
Handbook of Evolutionary Computation
Evolution strategies –A comprehensive introduction
Natural Computing: an international journal
A Seeded Memetic Algorithm for Large Unit Commitment Problems
Journal of Heuristics
Journal of Global Optimization
Introduction to Evolutionary Computing
Introduction to Evolutionary Computing
Stochastic Local Search: Foundations & Applications
Stochastic Local Search: Foundations & Applications
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Journal of Global Optimization
Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series)
Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series)
A comparative study of differential evolution variants for global optimization
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Multiobjective Problem Solving from Nature: From Concepts to Applications (Natural Computing Series)
Multiobjective Problem Solving from Nature: From Concepts to Applications (Natural Computing Series)
Advances in Differential Evolution
Advances in Differential Evolution
A memetic algorithm with adaptive hill climbing strategy for dynamic optimization problems
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
Super-fit control adaptation in memetic differential evolution frameworks
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
A Memetic Algorithm for Phylogenetic Reconstruction with Maximum Parsimony
EvoBIO '09 Proceedings of the 7th European Conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics
Computational Intelligence: An Introduction
Computational Intelligence: An Introduction
Multi-Objective Memetic Algorithms
Multi-Objective Memetic Algorithms
BiHEA: A Hybrid Evolutionary Approach for Microarray Biclustering
BSB '09 Proceedings of the 4th Brazilian Symposium on Bioinformatics: Advances in Bioinformatics and Computational Biology
Arc-guided evolutionary algorithm for the vehicle routing problem with time windows
IEEE Transactions on Evolutionary Computation
Memetic algorithm with extended neighborhood search for capacitated arc routing problems
IEEE Transactions on Evolutionary Computation
Research frontier: memetic computation-past, present & future
IEEE Computational Intelligence Magazine
Hybrid approaches and dimensionality reduction for portfolio selection with cardinality constraints
IEEE Computational Intelligence Magazine
Nature-Inspired Metaheuristic Algorithms: Second Edition
Nature-Inspired Metaheuristic Algorithms: Second Edition
Differential evolution with self adaptive local search
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Handbook of Memetic Algorithms
Handbook of Memetic Algorithms
Advances in artificial immune systems
IEEE Computational Intelligence Magazine
IEEE Transactions on Evolutionary Computation
Meta-Lamarckian learning in memetic algorithms
IEEE Transactions on Evolutionary Computation
Opposition-Based Differential Evolution
IEEE Transactions on Evolutionary Computation
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 Multi-Facet Survey on Memetic Computation
IEEE Transactions on Evolutionary Computation
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Differential evolution DE, like other probabilistic optimisation algorithms, sometimes exhibits premature convergence and stagnation. It is analysed by researchers that the DE is better in exploration of the search space compared to exploitation. In the solution search process of DE, there is enough chance to skip the true solution due to large step size. In order to balance the exploration and exploitation capability of the DE, a power law-based local search strategy is proposed and integrated with DE. In the proposed strategy, new solutions are generated around the best solution and it helps to enhance the exploitation capability of DE. The experiments on 14 un-biased test problems of different complexities show that the proposed strategy outperforms the basic DE and recent variants of DE namely, self-adaptive DE SaDE and scale factor local search DE SFLSDE in most of the experiments.