遗传算法是一种借鉴生物界自然选择和自然进化机制的搜索方法，通过对个体进行复制、交叉、变异操作完成搜索过程。
Genetic algorithms (GAs) are search algorithms based on of natural evolution processing including selection, mutation and crossover operations on the genes of individuals or potential solutions.
该算法基于单种群，在演化过程中直接对当前种群进行变异、交叉和选择操作，无须差异演化算法中的中间过渡种群。
The new algorithm was based on single population without intermediate population, in which mutation operation, crossover operation and selection operation were used on the current population.
采用了实数编码方案，对选择，交叉，变异等操作进行了改进。
The choosing, crossing and variation operation are improved using real-coded schema.
该算法对简单遗传算法的编码方式、选择策略、交叉和变异操作进行了改进，使搜索效率有了很大的提高，有效地避免了早期收敛。
This algorithm improves on encoding, selection, crossover and mutation operations of SGA. It enhances searching efficiency greatly, and avoids effectively premature convergence.
该算法对简单遗传算法的编码方式、选择策略、交叉和变异操作进行了改进，使搜索效率有了很大的提高，有效地避免了早期收敛。
This algorithm improves on encoding, selection, crossover and mutation operations of SGA. It enhances searching efficiency greatly, and avoids effectively premature convergence.
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