Optimization of control parameters for genetic algorithms
IEEE Transactions on Systems, Man and Cybernetics
A Taxonomy of Hybrid Metaheuristics
Journal of Heuristics
A Combined Swarm Differential Evolution Algorithm for Optimization Problems
Proceedings of the 14th International conference on Industrial and engineering applications of artificial intelligence and expert systems: engineering of intelligent systems
An enhanced memetic differential evolution in filter design for defect detection in paper production
Evolutionary Computation
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
Hybrid Evolutionary Algorithm for Solving Global Optimization Problems
HAIS '09 Proceedings of the 4th International Conference on Hybrid Artificial Intelligence Systems
A Memetic Differential Evolution Algorithm for Continuous Optimization
ISDA '09 Proceedings of the 2009 Ninth International Conference on Intelligent Systems Design and Applications
Meta-Lamarckian learning in memetic algorithms
IEEE Transactions on Evolutionary Computation
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The selection of the most appropriate Evolutionary Algorithm for a given optimization problem is a difficult task. Hybrid Evolutionary Algorithms are a promising alternative to deal with this problem. By means of the combination of different heuristic optimization approaches, it is possible to profit from the benefits of the best approach, avoiding the limitations of the others. Nowadays, there is an active research in the design of dynamic or adaptive hybrid algorithms. However, little research has been done in the automatic learning of the best hybridization strategy. This paper proposes a mechanism to learn a strategy based on the analysis of the results from past executions. The proposed algorithm has been evaluated on a well-known benchmark on continuous optimization. The obtained results suggest that the proposed approach is able to learn very promising hybridization strategies.