Editing Genetic algorithm (section)

==== Genetic operators ====
{{Main|Crossover (genetic algorithm)|Mutation (genetic algorithm)}}

The next step is to generate a second generation population of solutions from those selected, through a combination of [[genetic operator]]s: [[crossover (genetic algorithm)|crossover]] (also called recombination), and [[mutation (genetic algorithm)|mutation]].

For each new solution to be produced, a pair of "parent" solutions is selected for breeding from the pool selected previously. By producing a "child" solution using the above methods of crossover and mutation, a new solution is created which typically shares many of the characteristics of its "parents". New parents are selected for each new child, and the process continues until a new population of solutions of appropriate size is generated.
Although reproduction methods that are based on the use of two parents are more "biology inspired", some research<ref>Eiben, A. E. et al (1994). "Genetic algorithms with multi-parent recombination". PPSN III: Proceedings of the International Conference on Evolutionary Computation. The Third Conference on Parallel Problem Solving from Nature: 78&ndash;87. {{ISBN|3-540-58484-6}}.</ref><ref>Ting, Chuan-Kang (2005). "On the Mean Convergence Time of Multi-parent Genetic Algorithms Without Selection". Advances in Artificial Life: 403&ndash;412. {{ISBN|978-3-540-28848-0}}.</ref> suggests that more than two "parents" generate higher quality chromosomes.

These processes ultimately result in the next generation population of chromosomes that is different from the initial generation. Generally, the average fitness will have increased by this procedure for the population, since only the best organisms from the first generation are selected for breeding, along with a small proportion of less fit solutions. These less fit solutions ensure genetic diversity within the genetic pool of the parents and therefore ensure the genetic diversity of the subsequent generation of children.

Opinion is divided over the importance of crossover versus mutation. There are many references in [[David B. Fogel|Fogel]] (2006) that support the importance of mutation-based search.

Although crossover and mutation are known as the main genetic operators, it is possible to use other operators such as regrouping, colonization-extinction, or migration in genetic algorithms.{{citation needed|date=November 2019}}

It is worth tuning parameters such as the [[Mutation (genetic algorithm)|mutation]] probability, [[Crossover (genetic algorithm)|crossover]] probability and population size to find reasonable settings for the problem's [[complexity class]] being worked on. A very small mutation rate may lead to [[genetic drift]] (which is non-[[Ergodicity|ergodic]] in nature). A recombination rate that is too high may lead to premature convergence of the genetic algorithm. A mutation rate that is too high may lead to loss of good solutions, unless [[#Elitism|elitist selection]] is employed. An adequate population size ensures sufficient genetic diversity for the problem at hand, but can lead to a waste of computational resources if set to a value larger than required.