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| 双精英协同进化遗传算法 |
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刘全1,2, 王晓燕1, 傅启明1, 张永刚2, 章晓芳1,3
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1.苏州大学 计算机科学与技术学院, 江苏 苏州 215006;2.符号计算与知识工程教育部重点实验室(吉林大学), 吉林 长春 130012;3.计算机软件新技术国家重点实验室(南京大学), 江苏 南京 210093
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| 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(double elitecoevolutionary genetic algorithm,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. |
| 关键词: 遗传算法 进化算法 精英策略 协同进化 种群多样性 |
| DOI：10.3724/SP.J.1001.2012.04040 |
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| 基金项目:国家自然科学基金(60873116, 61070223, 61103045, 61170020); 江苏省自然科学基金(BK2008161, BK2009116); 江苏省高校自然科学研究项目(09KJA520002, 09KJB520012); 吉林大学符号计算与知识工程教育部重点实验室资助项目(93K172012K04) |
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| Double Elite Coevolutionary Genetic Algorithm |
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LIU Quan1,2, WANG Xiao-Yan1, FU Qi-Ming1, ZHANG Yong-Gang2, ZHANG Xiao-Fang1,3
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1.School of Computer Science and Technology, Soochow University, Suzhou 215006, China;2.Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education (Jilin University), Changchun 130012, China;3.State Key Laboratory for Novel Software Technology (Nanjing University), Nanjing 210093, China
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| Abstract: |
| 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. |
| Key words: genetic algorithm evolutionary algorithm elitist strategy coevolution population diversity |
