Optimization of control parameters for genetic algorithms
IEEE Transactions on Systems, Man and Cybernetics
Adaptive global optimization with local search
Adaptive global optimization with local search
Evolution and Optimum Seeking: The Sixth Generation
Evolution and Optimum Seeking: The Sixth Generation
HyGLEAM - An Approach to Generally Applicable Hybridization of Evolutionary Algorithms
PPSN VII Proceedings of the 7th International Conference on Parallel Problem Solving from Nature
Multimeme Algorithms for Protein Structure Prediction
PPSN VII Proceedings of the 7th International Conference on Parallel Problem Solving from Nature
An Evolutionary Algorithm for the Routing of Multi-Chip Modules
PPSN III Proceedings of the International Conference on Evolutionary Computation. The Third Conference on Parallel Problem Solving from Nature: Parallel Problem Solving from Nature
Real-coded memetic algorithms with crossover hill-climbing
Evolutionary Computation - Special issue on magnetic algorithms
Parameter control in evolutionary algorithms
IEEE Transactions on Evolutionary Computation
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
A cost-benefit-based adaptation scheme for multimeme algorithms
PPAM'07 Proceedings of the 7th international conference on Parallel processing and applied mathematics
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Important factors for the easy usage of an Evolutionary Algorithm (EA) are numbers of fitness calculations as low as possible, its robustness, and the reduction of its strategy parameters as far as possible. Multimeme Algorithms (MMA) are good candidates for the first two properties. In this paper a cost-benefit-based approach shall be introduced for the adaptive control of both meme selection and the ratio between local and global search. The latter is achieved by adaptively adjusting the intensity of the search of the memes and the frequency of their usage. It will be shown in which way the proposed kind of adaptation fills the gap previous work leaves. Detailed experiments in the field of continuous parameter optimisation demonstrate the superiority of the adaptive MMA over the simple MA and the pure EA.