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中文摘要：
针对传统遗传算法早熟收敛和收敛速度慢的问题，提出一种双精英协同进化遗传算法（doubleelitecoevolutionarygeneticalgorithm，简称DECGA）．该算法借鉴了精英策略和协同进化的思想，选择两个相异的、高适应度的个体（精英个体）作为进化操作的核心，两个精英个体分别按照不同的评价函数来选择个体,组成各自的进化子种群．两个子种群分别采用不同的进化策略，以平衡算法的勘探和搜索能力．理论分析证明，该算法具有全局收敛性．通过对测试函数的实验，其结果表明，该算法能搜索到几乎所有测试函数的最优解，同时能够有效地保持种群的多样性．与已有算法相比，该算法在收敛速度和搜索全局最优解上都有了较大的改进和提高．
英文摘要：
A new double elite coevolutionary genetic algorithm （DECGA） is proposed to avoid the premature convergence and low speed of convergence, based on the elite strategy and the concept of coevolutionary. In the DECGA, the two different and high fitness individuals （elite individuals） are selected as the core of the evolutionary operation, and the team members are selected by the different evaluation functions to form two teams through these two elite individuals. The two sub-populations can balance the capability of exploration and exploitation by the different evolutionary strategies. Theoretical analysis proves that the algorithm converges to the global optimization solution. Tests on the functions show that the algorithm can find the global optimal solution for most test functions, and it can also maintain the population diversity to a certain range. Compared with the existing algorithms, DECGA has a higher performance in precision of convergence and search efficiency.
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