Description: Global search ability of genetic algorithms, local search capability is weak, generally only be able to search a sub-optimal solution to the function optimization problem, rather than the optimal solution, especially the function has multiple peaks, genetic algorithm is easy to fall into the local polar can not find the true global optimal solution. Solving nonlinear programming due to the use of the gradient descent method, and has strong local search ability. Source code algorithm combines two advantages, on the one hand, the use of genetic algorithms for global search, on the other hand by nonlinear programming local search to obtain the global optimum function optimization problems. Experiments show that this method can not only solve the multimodal function optimization easy to fall into local minima problems, and has a highly iterative optimization efficiency, and achieved satisfactory results.
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GeneticNonlinearMatlab\Ackley.m
......................\AckleyStandardGAMain02.m
......................\AckleyStandardGAMain05.m
......................\AckleyStandardGAMain10.m
......................\AckleyStandardGANonMain02.m
......................\AckleyStandardGANonMain05.m
......................\AckleyStandardGANonMain10.m
......................\Code.m
......................\Cross.m
......................\FigureRead.m
......................\Griewank.m
......................\Mutation.m
......................\Nonlinear.m
......................\Rastrigin.m
......................\Schaffer.m
......................\Select.m
......................\Test.m
......................\TimeAckleyStandardGAMain02.m
......................\TimeAckleyStandardGAMain05.m
......................\TimeAckleyStandardGAMain10.m
......................\TimeAckleyStandardGANonMain02.m
......................\TimeAckleyStandardGANonMain05.m
......................\TimeAckleyStandardGANonMain10.m
......................\TimeGriewankStandardGAMain02.m
......................\TimeGriewankStandardGAMain05.m
......................\TimeGriewankStandardGAMain10.m
......................\TimeGriewankStandardGANonMain02.m
......................\TimeGriewankStandardGANonMain05.m
......................\TimeGriewankStandardGANonMain10.m
......................\TimeRastriginStandardGAMain02.m
......................\TimeRastriginStandardGAMain05.m
......................\TimeRastriginStandardGAMain10.m
......................\TimeRastriginStandardGANonMain02.m
......................\TimeRastriginStandardGANonMain05.m
......................\TimeRastriginStandardGANonMain10.m
......................\TimeSchafferStandardGAMain02.m
......................\TimeSchafferStandardGAMain05.m
......................\TimeSchafferStandardGAMain10.m
......................\TimeSchafferStandardGANonMain02.m
......................\TimeSchafferStandardGANonMain05.m
......................\TimeSchafferStandardGANonMain10.m
GeneticNonlinearMatlab
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