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| 2000 || [[Cartesian genetic programming]] || <ref>{{cite book |last1=Miller |first1=Julian F. |series=Natural Computing Series |chapter=Cartesian Genetic Programming |date=2011 |pages=17–34 |doi=10.1007/978-3-642-17310-3_2 |chapter-url=https://link.springer.com/chapter/10.1007/978-3-642-17310-3_2 |publisher=Springer |isbn=978-3-642-17309-7 |language=en}}</ref>
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| 2000 || Grammar-guided GP - Dynamic grammar pruning is applied in initialization|| <ref>{{cite book |last1=Ratle |first1=Alain |last2=Sebag |first2=Michèle |chapter=Genetic Programming and Domain Knowledge: Beyond the Limitations of Grammar-Guided Machine Discovery |title=Parallel Problem Solving from Nature PPSN VI |series=Lecture Notes in Computer Science |date=2000 |volume=1917 |pages=211–220 |doi=10.1007/3-540-45356-3_21 |chapter-url=https://link.springer.com/chapter/10.1007/3-540-45356-3_21 |publisher=Springer |isbn=978-3-540-41056-0 |url=https://hal.science/hal-00116116 |language=en}}</ref>
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| 2001 || [[Gene expression programming]] || <ref>{{cite arXiv |last1=Ferreira |first1=Candida |title=Gene Expression Programming: a New Adaptive Algorithm for Solving Problems |date=2001 |eprint=cs/0102027 }}</ref>
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| 2017 || Statistical GP - statistical information used to generate well-structured subtrees || <ref>{{cite journal |last1=Amir Haeri |first1=Maryam |last2=Ebadzadeh |first2=Mohammad Mehdi |last3=Folino |first3=Gianluigi |title=Statistical genetic programming for symbolic regression |journal=Applied Soft Computing |date=1 November 2017 |volume=60 |pages=447–469 |doi=10.1016/j.asoc.2017.06.050 |url=https://www.sciencedirect.com/science/article/abs/pii/S1568494617303939 |issn=1568-4946|url-access=subscription }}</ref>
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| 2018 || Multi-dimensional GP - novel program representation for multi-dimensional features || <ref>{{cite journal |last1=La Cava |first1=William |last2=Silva |first2=Sara |last3=Danai |first3=Kourosh |last4=Spector |first4=Lee |last5=Vanneschi |first5=Leonardo |last6=Moore |first6=Jason H. |title=Multidimensional genetic programming for multiclass classification |journal=Swarm and Evolutionary Computation |date=1 February 2019 |volume=44 |pages=260–272 |doi=10.1016/j.swevo.2018.03.015
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Morgan Kaufmann,
1999.
ISBN 978-1558605107</ref><ref>Garnett Wilson and Wolfgang Banzhaf. [http://www.cs.mun.ca/~banzhaf/papers/eurogp08_clgp.pdf "A Comparison of Cartesian Genetic Programming and Linear Genetic Programming"]</ref> The commercial GP software ''Discipulus'' uses automatic induction of binary machine code ("AIM")<ref>([[Peter Nordin]], 1997, Banzhaf et al., 1998, Section 11.6.2-11.6.3)</ref> to achieve better performance. ''μGP''<ref>{{cite web|url=https://ugp3.sourceforge.net/|title=μGP (MicroGP)|author=Giovanni Squillero}}</ref> uses [[directed multigraph]]s to generate programs that fully exploit the syntax of a given [[assembly language]]. [[Multi expression programming]] uses [[Three-address code]] for encoding solutions. Other program representations on which significant research and development have been conducted include programs for stack-based virtual machines,<ref>{{Cite web|url=http://gpbib.cs.ucl.ac.uk/gp-html/ieee94_perkis.html|title=Stack-Based Genetic Programming|website=gpbib.cs.ucl.ac.uk|language=en|access-date=2021-05-20}}</ref><ref name="Spector 7–40">{{Cite journal|last1=Spector|first1=Lee|last2=Robinson|first2=Alan|date=2002-03-01|title=Genetic Programming and Autoconstructive Evolution with the Push Programming Language|journal=Genetic Programming and Evolvable Machines|language=en|volume=3|issue=1|pages=7–40|doi=10.1023/A:1014538503543|s2cid=5584377|issn=1389-2576}}</ref><ref>{{Cite book|last1=Spector|first1=Lee|last2=Klein|first2=Jon|last3=Keijzer|first3=Maarten|title=Proceedings of the 7th annual conference on Genetic and evolutionary computation |chapter=The Push3 execution stack and the evolution of control |date=2005-06-25|publisher=ACM|pages=1689–1696|doi=10.1145/1068009.1068292|isbn=978-1595930101|citeseerx=10.1.1.153.384|s2cid=11954638}}</ref> and sequences of integers that are mapped to arbitrary programming languages via grammars.<ref>{{Cite book|title=Lecture Notes in Computer Science|last1=Ryan|first1=Conor|last2=Collins|first2=JJ|last3=Neill|first3=Michael O|date=1998|publisher=Springer Berlin Heidelberg|isbn=9783540643609|___location=Berlin, Heidelberg|pages=83–96|doi = 10.1007/bfb0055930|citeseerx = 10.1.1.38.7697}}</ref><ref>{{Cite journal|last1=O'Neill|first1=M.|last2=Ryan|first2=C.|s2cid=10391383|date=2001|title=Grammatical evolution|journal=IEEE Transactions on Evolutionary Computation|language=en-US|volume=5|issue=4|pages=349–358|doi=10.1109/4235.942529|bibcode=2001ITEC....5..349O |issn=1089-778X}}</ref> [[Cartesian genetic programming]] is another form of GP, which uses a graph representation instead of the usual tree based representation to encode computer programs.
Most representations have structurally noneffective code ([[intron]]s). Such non-coding genes may seem to be useless because they have no effect on the performance of any one individual. However, they alter the probabilities of generating different offspring under the variation operators, and thus alter the individual's [[variational properties]].
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