Zhou Zhongfu, Harris Kenneth D M
School of Chemistry, Cardiff University, Park Place, Cardiff, CF10 3AT, Wales.
Phys Chem Chem Phys. 2008 Dec 28;10(48):7262-9. doi: 10.1039/b807326k. Epub 2008 Oct 31.
A new strategy for implementing the concept of a "micro genetic algorithm" within a standard genetic algorithm (GA) procedure is proposed. The strategy operates by applying criteria to test for the occurrence of stagnation within the population of a standard GA calculation, and triggering the micro-GA procedure whenever stagnation is detected. The micro-GA is implemented in terms of the parallel evolution of a number of small sub-populations (comprising predominantly new randomly generated structures together with a few of the best structures from the stagnated population), and the sub-population of highest quality following the micro-GA procedure is used in the construction of the next population of the standard GA calculation. The micro-GA procedure is applied in the context of a GA for carrying out direct-space structure solution from powder X-ray diffraction data, and the results demonstrate that this strategy is an effective means of promoting structural diversity within a stagnated population, leading to significantly improved evolutionary progress. This strategy may prove to be more generally applicable as an approach for alleviating problems due to stagnation in GA calculations in other fields of application.
提出了一种在标准遗传算法（GA）过程中实现“微遗传算法”概念的新策略。该策略通过应用标准来测试标准GA计算群体中停滞现象的发生，并在检测到停滞时触发微GA过程。微GA是通过多个小子群体（主要由新随机生成的结构以及停滞群体中的一些最佳结构组成）的并行进化来实现的，微GA过程之后质量最高的子群体用于构建标准GA计算的下一个群体。微GA过程应用于从粉末X射线衍射数据进行直接空间结构解析的GA背景下，结果表明该策略是促进停滞群体中结构多样性的有效手段，从而显著改善进化进程。该策略可能被证明更普遍适用于缓解其他应用领域中GA计算因停滞而产生的问题的方法。
