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
Towards an adaptive multimeme algorithm for parameter optimisation suiting the engineers' needs
PPSN'06 Proceedings of the 9th international conference on Parallel Problem Solving from Nature
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
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Memetic Algorithms are the most frequently used hybrid of Evolutionary Algorithms (EA) for real-world applications. This paper will deal with one of the most important obstacles to their wide usage: compared to pure EA, the number of strategy parameters which have to be adjusted properly is increased. A cost-benefit-based adaptation scheme suited for every EA will be introduced, which leaves only one strategy parameter to the user, the population size. Furthermore, it will be shown that the range of feasible sizes can be reduced drastically.