A new evolutionary algorithm based on hybrid crossovers and intermittent mutation for global optimization of complex functions with high dimensions is proposed.
通过混合使用多种杂交算子并辅之以间歇变异，提出了一种求解高维复杂函数全局优化问题的新型演化算法。
Through embedding a gradient descend operator into the generic algorithm, a hybrid algorithm is achieved with fast convergence and great probability for global optimization.
在遗传算法中嵌入一个梯度下降算子，使得混合算法既有较快的收敛性，又能以较大概率得到全局极值。
Through embedding a gradient descend operator into the generic algorithm, a hybrid algorithm is achieved with fast convergence and great probability for global optimization.
在遗传算法中嵌入一个梯度下降算子，使得混合算法既有较快的收敛性，又能以较大概率得到全局极值。
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