Content deleted Content added
m Open access bot: url-access updated in citation with #oabot. |
PeterGrant07 (talk | contribs) m adding detail on early Genetic Programming and the intersection of robotic trajectory planning with sources |
||
Line 17:
Koza followed this with 205 publications on “Genetic Programming” (GP), name 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"/>
Line 86 ⟶ 92:
Sometimes two child crossover is used, in which case the removed subtree (in the animation on the left) is not simply deleted but is copied to a copy of the second parent (here on the right) replacing (in the copy) its randomly chosen subtree. Thus this type of subtree crossover takes two fit trees and generates two child trees.
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>
===Replication===
| |||