Description: The genetic algorithm is one simulation organic evolution mechanism stochastic global optimization reconnaissance method, has the very strong global optimization ability and robustness. The genetic algorithm belongs to the direct search method, to adapts the function basically unlimited, also does not request continual, also does not request the function differentiable, moreover did not need the initial information to be possible to seek the globally optimal solution to overcome the simplex algorithm initial condition to affect in a big way, easy to fall into is partially smallest and so on shortcomings, the ease of operation, the speed is quick, did not need the complex rule, and might use in the multi-objective optimizations, carried on the highly effective heuristic search in the solution space, might raise the operating speed.
- [fuzzy_pid] - two ways to control the performance comp
- [GAGJ).Rar] - improved GA. Fga.m for the main program
- [ga123245] - It is a genetic algorithm is used to opt
- [sgarar] - Bimodal and multimodal function of the g
- [GA_Matlab] - Some genetic algorithm Matlab examples,
- [GAexperiment] - matlab code for genetic algorithm
- [featureextraction] - Image processing and pattern recognition
- [opt11] - Source model search. Pattern search meth
- [1111] - Multi-objective Optimal Power Flow Calcu
- [NSGA2] - Multi-objective evolutionary algorithm,
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