Optimization of control parameters for genetic algorithms
IEEE Transactions on Systems, Man and Cybernetics
A Taxonomy of Hybrid Metaheuristics
Journal of Heuristics
Hyper-heuristics: Learning To Combine Simple Heuristics In Bin-packing Problems
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
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
Case-based heuristic selection for timetabling problems
Journal of Scheduling
A Memetic Differential Evolutionary Algorithm for High Dimensional Functions' Optimization
ICNC '07 Proceedings of the Third International Conference on Natural Computation - Volume 04
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
IWLCS'03-05 Proceedings of the 2003-2005 international conference on Learning classifier systems
Learning hybridization strategies in evolutionary algorithms
Intelligent Data Analysis
Soft Computing - A Fusion of Foundations, Methodologies and Applications - Special Issue on scalability of evolutionary algorithms and other metaheuristics for large-scale continuous optimization problems
Meta-Lamarckian learning in memetic algorithms
IEEE Transactions on Evolutionary Computation
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Hybrid Evolutionary Algorithms are a promising alternative to deal with the problem of selecting the most appropriate Evolutionary Algorithm for a specific problem. By means of the combination of different heuristic optimization approaches, it is possible to profit from the benefits of the best approach or, even more, to discover synergies between the algorithms that could improve the results of the best performing individual algorithm. Nowadays, there is an active research in the design of dynamic or adaptive combination strategies for hybrid algorithms. However, little research has been done in the automatic learning of the best hybridization strategy. This paper proposes a new methodology for developing intelligent adaptive hybrid algorithms that uses data mining techniques to analyze the results from past executions. The proposed methodology has been evaluated on a well-known benchmark on continuous optimization made up of 19 different functions and several dimensions 50, 100, 200 and 500. Several analyses have been conducted and statistical tests have been used for validating the results. The generated hybrid algorithm has achieved outstanding results, obtaining significantly better results than the MOS algorithm, the most performant algorithm on this benchmark, and the CMA-ES algorithm, one of the reference algorithms in continuous optimization.