Editing Genetic algorithm (section)

====Evolutionary algorithms====
{{More citations needed section|date=May 2011}}
{{main|Evolutionary algorithm}}
Evolutionary algorithms is a sub-field of [[Evolutionary Computation|evolutionary computing]].

* [[Evolution strategy|Evolution strategies]] (ES, see Rechenberg, 1994) evolve individuals by means of mutation and intermediate or discrete recombination. ES algorithms are designed particularly to solve problems in the real-value domain.<ref>{{cite book|last=Cohoon|first=J|display-authors=etal|title=Evolutionary algorithms for the physical design of VLSI circuits|url= https://www.ifte.de/mitarbeiter/lienig/cohoon.pdf |archive-url=https://ghostarchive.org/archive/20221009/https://www.ifte.de/mitarbeiter/lienig/cohoon.pdf |archive-date=2022-10-09 |url-status=live|journal=Advances in Evolutionary Computing: Theory and Applications|publisher= Springer, pp. 683-712, 2003|isbn=978-3-540-43330-9|year=2002}}</ref> They use self-adaptation to adjust control parameters of the search. De-randomization of self-adaptation has led to the contemporary Covariance Matrix Adaptation Evolution Strategy ([[CMA-ES]]).
* [[Evolutionary programming]] (EP) involves populations of solutions with primarily mutation and selection and arbitrary representations. They use self-adaptation to adjust parameters, and can include other variation operations such as combining information from multiple parents.
* [[Estimation of Distribution Algorithm]] (EDA) substitutes traditional reproduction operators by model-guided operators. Such models are learned from the population by employing machine learning techniques and represented as Probabilistic Graphical Models, from which new solutions can be sampled<ref>{{cite book|last1=Pelikan|first1=Martin|last2=Goldberg|first2=David E.|last3=Cantú-Paz|first3=Erick|title=BOA: The Bayesian Optimization Algorithm|journal=Proceedings of the 1st Annual Conference on Genetic and Evolutionary Computation - Volume 1|date=1 January 1999|pages=525–532|url=http://dl.acm.org/citation.cfm?id=2933973|isbn=9781558606111|series=Gecco'99}}</ref><ref>{{cite book|last1=Pelikan|first1=Martin|title=Hierarchical Bayesian optimization algorithm : toward a new generation of evolutionary algorithms|date=2005|publisher=Springer|location=Berlin [u.a.]|isbn=978-3-540-23774-7|edition=1st}}</ref> or generated from guided-crossover.<ref>{{cite book|last1=Thierens|first1=Dirk|title=Parallel Problem Solving from Nature, PPSN XI |chapter=The Linkage Tree Genetic Algorithm|date=11 September 2010|pages=264–273|doi=10.1007/978-3-642-15844-5_27|language=en|isbn=978-3-642-15843-8}}</ref>
* [[Genetic programming]] (GP) is a related technique popularized by [[John Koza]] in which computer programs, rather than function parameters, are optimized. Genetic programming often uses [[Tree (data structure)|tree-based]] internal [[data structure]]s to represent the computer programs for adaptation instead of the [[List (computing)|list]] structures typical of genetic algorithms. There are many variants of Genetic Programming, including [[Cartesian genetic programming]], [[Gene expression programming]],<ref>{{cite journal|last=Ferreira|first=C|title=Gene Expression Programming: A New Adaptive Algorithm for Solving Problems|url= http://www.gene-expression-programming.com/webpapers/GEP.pdf |archive-url=https://ghostarchive.org/archive/20221009/http://www.gene-expression-programming.com/webpapers/GEP.pdf |archive-date=2022-10-09 |url-status=live|journal=Complex Systems |year=2001|volume=13 |issue=2 |pages=87–129|arxiv=cs/0102027|bibcode=2001cs........2027F}}</ref> [[grammatical evolution]], [[Linear genetic programming]], [[Multi expression programming]] etc.
* [[Grouping genetic algorithm]] (GGA) is an evolution of the GA where the focus is shifted from individual items, like in classical GAs, to groups or subset of items.<ref name="Falkenauer">{{cite book|last=Falkenauer|first=Emanuel|author-link=Emanuel Falkenauer|year=1997|title=Genetic Algorithms and Grouping Problems|publisher=John Wiley & Sons Ltd|location=Chichester, England|isbn=978-0-471-97150-4}}</ref> The idea behind this GA evolution proposed by [[Emanuel Falkenauer]] is that solving some complex problems, a.k.a. ''clustering'' or ''partitioning'' problems where a set of items must be split into disjoint group of items in an optimal way, would better be achieved by making characteristics of the groups of items equivalent to genes. These kind of problems include [[bin packing problem|bin packing]], line balancing, [[cluster analysis|clustering]] with respect to a distance measure, equal piles, etc., on which classic GAs proved to perform poorly. Making genes equivalent to groups implies chromosomes that are in general of variable length, and special genetic operators that manipulate whole groups of items. For bin packing in particular, a GGA hybridized with the Dominance Criterion of Martello and Toth, is arguably the best technique to date.
* [[Interactive evolutionary algorithm]]s are evolutionary algorithms that use human evaluation. They are usually applied to domains where it is hard to design a computational fitness function, for example, evolving images, music, artistic designs and forms to fit users' aesthetic preference.