Postgresql 中文操作指南
62.2. Genetic Algorithms #
遗传算法（GA）是一种通过随机搜索操作的启发式优化方法。优化问题的可能解决方案集合被认为是 population individuals。个体对环境的适应程度由其 fitness 指定。
The genetic algorithm (GA) is a heuristic optimization method which operates through randomized search. The set of possible solutions for the optimization problem is considered as a population of individuals. The degree of adaptation of an individual to its environment is specified by its fitness.
个人在搜索空间中的坐标由 chromosomes 表示，本质上是一组字符字符串。gene 是染色体的一个子部分，它编码要优化的单个参数的值。基因的典型编码可以是 binary 或 integer。
The coordinates of an individual in the search space are represented by chromosomes, in essence a set of character strings. A gene is a subsection of a chromosome which encodes the value of a single parameter being optimized. Typical encodings for a gene could be binary or integer.
通过模拟进化操作 recombination、mutation 和 selection，发现了新一代搜索点，这些搜索点显示出比其祖先更高的平均适应度。 Figure 62.1 说明了这些步骤。
Through simulation of the evolutionary operations recombination, mutation, and selection new generations of search points are found that show a higher average fitness than their ancestors. Figure 62.1 illustrates these steps.
Figure 62.1. Structure of a Genetic Algorithm
根据 comp.ai.genetic FAQ，不能过分强调，GA 并不是解决问题的纯粹随机搜索。GA 使用随机过程，但结果明显是非随机的（比随机的要好）。
According to the comp.ai.genetic FAQ it cannot be stressed too strongly that a GA is not a pure random search for a solution to a problem. A GA uses stochastic processes, but the result is distinctly non-random (better than random).