基因演算法 (GA) 為最著名也最廣為使用的演化式演算法。基因演算法完全承襲了演化式計算的原則: 模仿自然界的演化機制，其中包含了選拔 (selection)、交換 (crossover, 或稱之為重組 recombination)、突變 (mutation)。基於達爾文的 "適者生存" 理論，基因演算法被認為能在電腦世界"演化"出一個問題的最佳解。

基因演算法的程序如下 :

GAs encode candidate solutions as chromosomes. The way of encoding chromosomes is referred to as representation, which is essentially related to the problem to be solved. For example, bit strings are widely used in functional optimization problems while orders (sequences) are used in combinatorial problems. Depending on representation, GAs can be classified into three genres: binary-coded GAs, real-coded GAs, and order-based GAs. Instead of a single chromosome, GAs evolve with a set of chromosomes, called the population. The fitness function is devised to evaluate the quality (fitness) of candidate solutions (chromosomes). Intuitively, for a maximization problem, the better the solution, the higher the fitness.

The evolution of GAs begins with initialization of the population. Afterwards, GAs embark on the process of reproduction. First, the selection operator picks two chromosomes from the population to serve as parents. Next, GAs perform crossover on these two parents to reproduce their offspring. The predetermined probability, crossover rate, defines the probability to perform crossover. Analogously, mutation is performed with a probability, mutation rate, on the offspring reproduced by crossover to slightly alter some genes. This process of reproduction repeats until the set of offspring, called the subpopulation, is filled. Acting on "Survival of the Fittest", the survivor operator draws the fittest chromosomes out of the subpopulation with (or without) the primitive population; the chosen chromosomes will constitute the population for the next generation. Following this procedure, GAs evolve until a predetermined termination criterion is met.