Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
A comprehensive survey of fitness approximation in evolutionary computation
Soft Computing - A Fusion of Foundations, Methodologies and Applications
A framework for evolutionary optimization with approximate fitnessfunctions
IEEE Transactions on Evolutionary Computation
Hybrid methods using genetic algorithms for global optimization
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
How genetic algorithms can improve a pacemaker efficiency
Proceedings of the 9th annual conference companion on Genetic and evolutionary computation
Multiobjective optimization of a stent in a fluid-structure context
Proceedings of the 10th annual conference companion on Genetic and evolutionary computation
A fully adaptive hybrid optimization of aircraft engine blades
Journal of Computational and Applied Mathematics
An optimal reconstruction of the human arterial tree from doppler echotracking measurements
Proceedings of the 14th annual conference companion on Genetic and evolutionary computation
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Various global optimization methods are compared in order to find the best strategy to solve realistic drag reduction problems in the automotive industry. All the methods consist in improving classical genetic algorithms, either by coupling them with a deterministic descent method or by incorporating a fast but approximated evaluation process. The efficiency of these methods (called HM and AGA respectively) is shown and compared, first on analytical test functions, then on a drag reduction problem where the computational time of a GA is reduced by a factor up to 7.