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林业科学 ›› 2018, Vol. 54 ›› Issue (4): 30-37.doi: 10.11707/j.1001-7488.20180404

• 论文与研究报告 • 上一篇    下一篇

改进型遗传算法在种子园无性系配置设计中的应用

王晴1, 齐建东1, 崔晓晖1, 李伟2   

  1. 1. 北京林业大学信息学院 北京 100083;
    2. 北京林业大学生物科学与技术学院 北京 100083
  • 收稿日期:2017-05-24 修回日期:2017-09-06 出版日期:2018-04-25 发布日期:2018-05-28
  • 基金资助:
    国家重点研发计划"西北干旱荒漠区煤炭基地生态安全保障技术"(2017YFC0504400);国家重点研发计划"樟子松速生建筑材林高效培育技术研究"(2017YFD0600505);中央高校基本科研业务费专项资金(BLX2014-27)。

Application of Improved Genetic Algorithm in Clonal Deployment for Seed Orchard

Wang Qing1, Qi Jiandong1, Cui Xiaohui1, Li Wei2   

  1. 1. School of Information Science and Technology, Beijing Forestry University Beijing 100083;
    2. College of Biological Sciences and Technology, Beijing Forestry University Beijing 100083
  • Received:2017-05-24 Revised:2017-09-06 Online:2018-04-25 Published:2018-05-28

摘要: [目的]使用优化算法优化种子园无性系配置的设计方案,以保证种子园子代在具有较高的遗传增益的前提下维持丰富的遗传多样性,为高世代种子园的无性系配置设计提供参考。[方法]基于已有的内蒙古红花尔基樟子松国家良种基地的樟子松亲本为材料,采用SSR分子标记技术及其分析软件确定樟子松亲本之间的遗传距离,使用优化算法开展基于遗传距离的樟子松种子园无性系配置优化设计,并基于研究目标改进原有的优化方法,最后与传统的种子园无性系配置方案、其他优化方法得到的方案进行对比分析。[结果]使用本文改进的优化算法获得的种子园无性系配置方案优于传统的顺序错位以及其他方法的种子园配置方案,该方法能够使遗传距离较近的无性系在配置上保持最大距离,减少近亲交配机会,在一定程度上扩大了子代的遗传基础。[结论]当已知种子园无性系亲本间遗传距离时,可利用本文提出的基于传统遗传算法进行改进的多种群并行自适应的方法,即改进型自适应并行遗传算法,来实现基于遗传距离的种子园无性系优化配置。

关键词: 种子园, 无性系配置, 遗传距离, 遗传算法

Abstract: [Objective]The optimization algorithm was used to optimize the design of clonal distribution of the seed orchard, so as to ensure rich genetic diversity in the next generation while maintaining high genetic gain, and provide advice on the clonal distribution of the high generation seed orchard.[Method]The genetic distance between the parents of Pinus sylvestris var. mongolica was determined using SSR markers and relevant analysis software, the parent trees were selected from the existing P. sylvestris var. mongolica in the National Production Base of Improved Seeds in Honghuaerji of Inner Mongolia. Based on this genetic relationship, the optimization algorithm was used to optimize the clonal deployment by computer simulations, and the original optimization method was improved by accommodating the objectives of the current study. Finally, the optimized clonal deployment was compared with the traditional clonal deployment and other options in a seed orchard.[Result]The seed orchard clonal deployment plan created by optimization algorithm is better than the traditional sequential dislocation method and other methods. This method can keep the maximum distance between the clones which have closer genetic relationship and reduce the chance of inbreeding, therefore enlarging the genetic basis of the offspring to a certain extent.[Conclusion] When the genetic distances between parents were given, the Improved Adaptive Parallel Genetic Algorithm, which was improved by changing the adaptive method based on the traditional Genetic Algorithm, can be used to optimize clonal deployment in seed orchard based on genetic distance.

Key words: seed orchard, clonal deployment, genetic distance, genetic algorithm

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