中文说明：
遗传算法是计算数学中用于解决最佳化的搜索算法，是进化算法的一种。进化算法最初是借鉴了进化生物学中的一些现象而发展起来的，这些现象包括遗传、突变、自然选择以及杂交等。遗传算法通常实现方式为一种计算机模拟。对于一个最优化问题，一定数量的候选解（称为个体）的抽象表示（称为染色体）的种群向更好的解进化。传统上，解用二进制表示（即0和1的串），但也可以用其他表示方法。进化从完全随机个体的种群开始，之后一代一代发生。在每一代中，整个种群的适应度被评价，从当前种群中随机地选择多个个体（基于它们的适应度），通过自然选择和突变产生新的生命种群，该种群在算法的下一次迭代中成为当前种群。
English Description：
Genetic algorithm is a kind of evolutionary algorithm, which is used to solve the optimization problem in computational mathematics. Evolutionary algorithm was developed from some phenomena in evolutionary biology, including heredity, mutation, natural selection and hybridization. Genetic algorithm is usually realized by computer simulation. For an optimization problem, the population with a certain number of abstract representations (called chromosomes) of candidate solutions (called individuals) evolves to a better solution. Traditionally, the solution is expressed in binary (that is, the string of 0 and 1), but it can also be expressed in other ways. Evolution begins with a population of completely random individuals, and then occurs from generation to generation. In each generation, the fitness of the whole population is evaluated. Multiple individuals are randomly selected from the current population (based on their fitness), and a new life population is generated through natural selection and mutation. The population becomes the current population in the next iteration of the algorithm.