Editing Evolutionary algorithm (section)

== Related techniques and other global search methods ==
There are some other proven and widely used methods of nature inspired global search techniques such as

* [[Memetic algorithm]] – A hybrid method, inspired by [[Richard Dawkins]]'s notion of a meme. It commonly takes the form of a population-based algorithm (frequently an EA) coupled with individual learning procedures capable of performing local refinements. Emphasizes the exploitation of problem-specific knowledge and tries to orchestrate local and global search in a synergistic way.
* A [[Population model (evolutionary algorithm)#Neighbourhood models or cellular evolutionary algorithms|cellular evolutionary or memetic algorithm]] uses a topological neighbouhood relation between the individuals of a population for restricting the mate selection and by that reducing the propagation speed of above-average individuals. The idea is to maintain genotypic diversity in the poulation over a longer period of time to reduce the risk of premature convergence.
* [[Ant colony optimization]] is based on the ideas of ant foraging by pheromone communication to form paths. Primarily suited for [[combinatorial optimization]] and [[Graph theory|graph]] problems.
* [[Particle swarm optimization]] is based on the ideas of animal flocking behaviour. Also primarily suited for [[numerical optimization]] problems.
* [[Gaussian adaptation]] – Based on information theory. Used for maximization of manufacturing yield, [[mean fitness]] or [[average information]]. See for instance [[Entropy in thermodynamics and information theory]].

In addition, many new nature-inspired or methaphor-guided algorithms have been proposed since the beginning of this century. For criticism of most publications on these, see the remarks at the end of the introduction to the article on [[metaheuristic]]s.