Genetic programming: Difference between revisions

{{Distinguish|Generic programming|Genetic engineering|DNA computing}}

It may and often does happen that a particular run of the algorithm results in premature convergence to some local maximum that is not a globally optimal or even good solution.  Multiple runs (dozens to hundreds) are usually necessary to produce a very good result.  It may also be necessary to have a large starting population size and variability of the individuals to avoid pathologies.

Although the idea of evolving programs, initially in the computer language [[Lisp (programming language)|Lisp]], was current amongst John Holland's students,<ref>A personal communication with [http://www.dcs.bbk.ac.uk/~tom/ Tom Westerdale]</ref> it was not until they organised the first [[Genetic algorithm|Genetic Algorithms]] (GA) conference in Pittsburgh that Nichael Cramer<ref>{{Cite web|url=https://www.cs.bham.ac.uk/~wbl/biblio/gp-html/icga85_cramer.html|title=A representation for the Adaptive Generation of Simple Sequential Programs|website=www.cs.bham.ac.uk|language=en|access-date=2018-05-19}}</ref> published evolved programs in two specially designed languages, which included the first statement of modern "tree-based" genetic programming (that is, procedural languages organized in tree-based structures and operated on by suitably defined GA-operators). In 1988, [[John Koza]] (also a PhD student of John Holland) patented his invention of a GA for program evolution.<ref>{{Cite web|url=https://www.cs.bham.ac.uk/~wbl/biblio/gp-html/Koza_1990_pat-GAsp.html|title=Non-Linear Genetic Algorithms for Solving Problems|website=www.cs.bham.ac.uk|language=en|access-date=2018-05-19}}</ref> This was followed by publication in the International Joint Conference on Artificial Intelligence IJCAI-89.<ref>{{Cite web|url=https://www.cs.bham.ac.uk/~wbl/biblio/gp-html/Koza89.html|title=Hierarchical genetic algorithms operating on populations of computer programs|website=www.cs.bham.ac.uk|language=en|access-date=2018-05-19}}</ref>

Koza followed this with 205 publications on "genetic programming", a term coined by David Goldberg, also a PhD student of John Holland.<ref>Goldberg. D.E. (1983), Computer-aided gas pipeline operation using genetic algorithms and rule learning. Dissertation presented to the University of Michigan at Ann Arbor, Michigan, in partial fulfillment of the requirements for Ph.D.</ref> However, it is the series of 4 books by Koza, starting in 1992<ref>{{Cite web|url=https://www.cs.bham.ac.uk/~wbl/biblio/gp-html/koza_book.html|title=Genetic Programming: On the Programming of Computers by Means of Natural Selection|website=www.cs.bham.ac.uk|language=en|access-date=2018-05-19}}</ref> with accompanying videos,<ref>{{Cite web|url=https://www.youtube.com/watch?v=tTMpKrKkYXo| archive-url=https://ghostarchive.org/varchive/youtube/20211211/tTMpKrKkYXo| archive-date=2021-12-11 | url-status=live|title=Genetic Programming:The Movie|website=gpbib.cs.ucl.ac.uk| date=16 December 2020|language=en|access-date=2021-05-20}}{{cbignore}}</ref> that really established GP. Subsequently, there was an enormous expansion of the number of publications with the Genetic Programming Bibliography, surpassing 10,000 entries.<ref>{{Cite web|url=http://gpbib.cs.ucl.ac.uk/gp-html/Hu_2014_Alife.html|title=The effects of recombination on phenotypic exploration and robustness in evolution|website=gpbib.cs.ucl.ac.uk|language=en|access-date=2021-05-20}}</ref> In 2010, Koza<ref>{{Cite web|url=https://www.cs.bham.ac.uk/~wbl/biblio/gp-html/Koza_2010_GPEM.html|title=Human-competitive results produced by genetic programming|website=www.cs.bham.ac.uk|language=en|access-date=2018-05-20}}</ref> listed 77 results where genetic programming was human competitive.

The departure of GP from the rigid, fixed-length representations typical of early GA models was not entirely without precedent. Early work on variable-length representations laid the groundwork. One notable example is messy genetic algorithms, which introduced irregular, variable-length chromosomes to address building block disruption and positional bias in standard GAs.<ref>Goldberg, D.E., Korb, B., & Deb, K. (1989). Messy Genetic Algorithms: Motivation, Analysis, and First Results. Complex Systems, 3, 493–530.</ref> Another precursor was robot trajectory programming, where genome representations encoded program instructions for robotic movements—structures inherently variable in length.<ref>Davidor, Y. (1989). Analogous Crossover. In Proceedings of the 3rd International Conference on Genetic Algorithms (pp. 98–103). Morgan Kaufmann.</ref> Even earlier, unfixed-length representations were proposed in a doctoral dissertation by Cavicchio, who explored adaptive search using simulated evolution. His work provided foundational ideas for flexible program structures.<ref>Cavicchio, D.J. (1970). Adaptive Search Using Simulated Evolution. Doctoral dissertation, University of Michigan, Ann Arbor.</ref>

In 1996, Koza started the annual Genetic Programming conference,<ref>{{Cite web|url=https://www.cs.bham.ac.uk/~wbl/biblio/gp-html/koza_gp96.html|title=Genetic Programming 1996: Proceedings of the First Annual Conference|website=www.cs.bham.ac.uk|language=en|access-date=2018-05-19}}</ref> which was followed in 1998 by the annual EuroGP conference,<ref>{{Cite web|url=https://www.cs.bham.ac.uk/~wbl/biblio/gp-html/banzhaf_1998_GP.html|title=Genetic Programming|website=www.cs.bham.ac.uk|language=en|access-date=2018-05-19}}</ref> and the first book<ref>{{Cite web|url=https://www.cs.bham.ac.uk/~wbl/biblio/gp-html/langdon_book.html|title=Genetic Programming and Data Structures: Genetic Programming + Data Structures = Automatic Programming!|website=www.cs.bham.ac.uk|language=en|access-date=2018-05-20}}</ref> in a GP series edited by Koza. 1998 also saw the first GP textbook.<ref name="cs.bham.ac.uk">{{Cite web|url=https://www.cs.bham.ac.uk/~wbl/biblio/gp-html/banzhaf_1997_book.html|title=Genetic Programming – An Introduction; On the Automatic Evolution of Computer Programs and its Applications|website=www.cs.bham.ac.uk|language=en|access-date=2018-05-20}}</ref> GP continued to flourish, leading to the first specialist GP journal<ref>{{Cite journal|last=Banzhaf|first=Wolfgang|date=2000-04-01|title=Editorial Introduction|journal=Genetic Programming and Evolvable Machines|language=en|volume=1|issue=1–2|pages=5–6|doi=10.1023/A:1010026829303|issn=1389-2576}}</ref> and three years later (2003) the annual Genetic Programming Theory and Practice (GPTP) workshop was established by Rick Riolo.<ref>{{Cite web|url=https://www.cs.bham.ac.uk/~wbl/biblio/gp-html/RioloWorzel_2003.html|title=Genetic Programming Theory and Practice|website=www.cs.bham.ac.uk|language=en|access-date=2018-05-20}}</ref><ref name="field guide">{{Cite web|url=http://www.gp-field-guide.org.uk/|title=A Field Guide to Genetic Programming|website=www.gp-field-guide.org.uk|access-date=2018-05-20}}</ref> Genetic programming papers continue to be published at a diversity of conferences and associated journals. Today there are nineteen GP books including several for students.<ref name="cs.bham.ac.uk"/>

|+ Timeline of EP – selected algorithms<ref name=overview>{{cite journal |last1=Slowik |first1=Adam |last2=Kwasnicka |first2=Halina |title=Evolutionary algorithms and their applications to engineering problems |journal=Neural Computing and Applications |date=1 August 2020 |volume=32 |issue=16 |pages=12363–12379 |doi=10.1007/s00521-020-04832-8 |language=en |issn=1433-3058|doi-access=free }}</ref>

| 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>

| 2012 || Multi-gene GP – Combination of classical method for parameter estimation and structure selection || <ref>{{cite journal |last1=Gandomi |first1=Amir Hossein |last2=Alavi |first2=Amir Hossein |title=A new multi-gene genetic programming approach to nonlinear system modeling. Part I: materials and structural engineering problems |journal=Neural Computing and Applications |date=1 February 2012 |volume=21 |issue=1 |pages=171–187 |doi=10.1007/s00521-011-0734-z |url=https://link.springer.com/article/10.1007/s00521-011-0734-z |language=en |issn=1433-3058|url-access=subscription }}</ref>

| 2012 || Geometric semantic GP – Direct search in the space of the underlying semantics of the programs || <ref>{{cite book |last1=Moraglio |first1=Alberto |last2=Krawiec |first2=Krzysztof |last3=Johnson |first3=Colin G. |chapter=Geometric Semantic Genetic Programming |title=Parallel Problem Solving from Nature – PPSN XII |series=Lecture Notes in Computer Science |date=2012 |volume=7491 |pages=21–31 |doi=10.1007/978-3-642-32937-1_3 |chapter-url=https://link.springer.com/chapter/10.1007/978-3-642-32937-1_3 |publisher=Springer |isbn=978-3-642-32936-4 |language=en}}</ref>

| 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>

| 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 |issn=2210-6502|doi-access=free }}</ref>

Early work that set the stage for current genetic programming research topics and applications is diverse, and includes [[program synthesis|software synthesis]] and repair, predictive modeling, data mining,<ref>{{Cite web|url=https://www.cs.bham.ac.uk/~wbl/biblio/gp-html/freitas_2002_book.html|title=Data Mining and Knowledge Discovery with Evolutionary Algorithms|website=www.cs.bham.ac.uk|language=en|access-date=2018-05-20}}</ref> financial modeling,<ref>{{Cite web|url=https://www.cs.bham.ac.uk/~wbl/biblio/gp-html/tsang_1998_eddie.html|title=EDDIE beats the bookies|website=www.cs.bham.ac.uk|language=en|access-date=2018-05-20}}</ref> soft sensors,<ref>{{Cite web|url=https://www.cs.bham.ac.uk/~wbl/biblio/gp-html/Kordon_book.html|title=Applying Computational Intelligence How to Create Value|website=www.cs.bham.ac.uk|language=en|access-date=2018-05-20}}</ref> design,<ref>{{Cite web|url=https://www.cs.bham.ac.uk/~wbl/biblio/gp-html/DBLP_journals_aiedam_Koza08.html|title=Human-competitive machine invention by means of genetic programming|website=www.cs.bham.ac.uk|language=en|access-date=2018-05-20}}</ref> and image processing.<ref>{{Cite journal |last=Lam |first=Brian |last2=Ciesielski |first2=Vic |date=2004 |editor-last=Deb |editor-first=Kalyanmoy |title=Discovery of Human-Competitive Image Texture Feature Extraction Programs Using Genetic Programming |url=https://link.springer.com/chapter/10.1007/978-3-540-24855-2_121?error=cookies_not_supported&code=f8219d96-15e2-4c6c-ab95-779543bbb11d |journal=Genetic and Evolutionary Computation – GECCO 2004 |language=en |location=Berlin, Heidelberg |publisher=Springer |pages=1114–1125 |doi=10.1007/978-3-540-24855-2_121 |isbn=978-3-540-24855-2|url-access=subscription }}</ref> Applications in some areas, such as design, often make use of intermediate representations,<ref>{{Cite web|url=https://www.cs.bham.ac.uk/~wbl/biblio/gp-html/bentley_1999_TWGDACEEDP.html|title=Three Ways to Grow Designs: A Comparison of Embryogenies for an Evolutionary Design Problem|website=www.cs.bham.ac.uk|language=en|access-date=2018-05-20}}</ref> such as Fred Gruau's cellular encoding.<ref>{{Cite journal|url=https://ieeexplore.ieee.org/document/243137|title=Cellular encoding as a graph grammar – IET Conference Publication|pages=17/1–1710|website=[[IEEE]]|language=en-US|access-date=2018-05-20|date=April 1993}}</ref> Industrial uptake has been significant in several areas including finance, the chemical industry, bioinformatics<ref>{{Cite web|url=https://www.cs.bham.ac.uk/~wbl/biblio/gp-html/taylor_1998_gadiirsab.html|title=Genetic Algorithm Decoding for the Interpretation of Infra-red Spectra in Analytical Biotechnology|website=www.cs.bham.ac.uk|language=en|access-date=2018-05-20}}</ref><ref>{{Cite web|url=https://www.cs.bham.ac.uk/~wbl/biblio/gp-html/langdon_2004_GPEM.html|title=Genetic Programming for Mining DNA Chip data from Cancer Patients|website=www.cs.bham.ac.uk|language=en|access-date=2018-05-20}}</ref> and the steel industry.<ref>{{Cite web|url=https://www.cs.bham.ac.uk/~wbl/biblio/gp-html/Kovacic_2009_MMP2.html|title=Genetic Programming and Jominy Test Modeling|website=www.cs.bham.ac.uk|language=en|access-date=2018-05-20}}</ref>

{{Main|Genetic representation}}

Non-tree representations have been suggested and successfully implemented, such as [[linear genetic programming]], which perhaps suits the more traditional [[imperative languages]].<ref>Genetic Programming: An Introduction,

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.

* '''Ramped half-and-half''' creates a population consisting of <math>md-1</math> parts and a maximum depth of <math>md</math> for its trees. The first part has a maximum depth of 2, second of 3 and so on up to the <math>md-1</math>-th part with maximum depth <math>md</math>. Half of every part is created by ''Grow'', while the other part is created by ''Full''.

In genetic programming two fit individuals are chosen from the population to be parents for one or two children. In tree genetic programming, these parents are represented as inverted lisp like trees, with their root nodes at the top. In subtree crossover in each parent a subtree is randomly chosen. (Highlighted with yellow in the animation.) In the root donating parent (in the animation on the left) the chosen subtree is removed and replaced with a copy of the randomly chosen subtree from the other parent, to give a new child tree.

The tree-based approach in genetic programming also shares structural and procedural similarities with earlier knowledge-based and topology-oriented crossover methods. Specifically, analogous crossover and homologous crossover, both implemented in robot trajectory planning, exhibit a resemblance to subtree operations in tree GP. These crossover mechanisms were described in the context of heuristic optimisation strategies in robotics.<ref>Davidor, Y. (1991). Genetic Algorithms and Robotics: A Heuristic Strategy for Optimization. World Scientific Series in Robotics and Intelligent Systems: Volume 1.</ref>

GP has been successfully used as an [[automatic programming]] tool, a machine learning tool and an automatic problem-solving engine.<ref name="field guide" /> GP is especially useful in the domains where the exact form of the solution is not known in advance or an approximate solution is acceptable (possibly because finding the exact solution is very difficult). Some of the applications of GP are curve fitting, data modeling, [[symbolic regression]], [[feature selection]], and classification. John R. Koza mentions 76 instances where genetic programming has been able to produce results that are competitive with human-produced results (called human-competitive results).<ref>{{Cite journal|last=Koza|first=John R|title=Human-competitive results produced by genetic programming|journal=Genetic Programming and Evolvable Machines|volume=11|issue=3–4|pages=251–284|language=en-US|doi=10.1007/s10710-010-9112-3|year=2010|doi-access=free}}</ref> Since 2004, the annual Genetic and Evolutionary Computation Conference ([[GECCO]]) holds a Human Competitive Awards (called Humies) competition,<ref>{{cite web|url=http://www.human-competitive.org/awards|title=Humies =Human-Competitive Awards}}</ref> where cash awards are presented to human-competitive results produced by any form of genetic and evolutionary computation. GP has won many awards in this competition over the years.

Critics of this idea often say this approach is overly broad in scope. However, it might be possible to constrain the fitness criterion onto a general class of results, and so obtain an evolved GP that would more efficiently produce results for sub-classes. This might take the form of a meta evolved GP for producing human walking algorithms, which is then used to evolve human running, jumping, etc. The fitness criterion applied to the meta GP would simply be one of efficiency.