To what extent does imitating biological evolution benefit genetic programming?<\/p>\n
Genetic algorithms are a set of search algorithms that have been inspired by biological evolution [holland]. They have been used in applications from *** to creative logo design[blprnt], by using variations of biological mutation, fitness-based selection, and populations.<\/p>\n
Over the years, the benefits provided by copying biology have been debated. For example, the inclusion of sexual recombination, which is a part of nearly all plant and animal reproduction, drastically degrades the performance of genetic algorithms [nordin] something which evolutionary biologists still don’t have a computationally sound explanation for in biology (R. Redfield, personal communication, February 4, 2010). On the other hand, biological evolution has produced solutions to many different environmental conditions – from the darkest sea-floors, to the driest deserts – which could be used to inform computer science.<\/p>\n
There is already a selection of literature on genetic algorithms which would allow a review to explore which aspects of biological evolution are worth emulating, and which have been unhelpful to the field of computer science.<\/p>\n
By summarizing the state of the art of genetic algorithms, and comparing that with an introductory understanding of biology, I hope to describe several mechanisms of biological evolution, how they transfer to genetic algorithms, and present a base comparison of whether they are useful in computer science. Due to the situational nature of the search problems to which genetic algorithms are applied, I would expect categorical classification of benefit of these mechanisms to be difficult.<\/p>\n
[holland]
\nHolland, John H. Adaptation in Natural and
\nArtificial Systems. Ann Arbor, MI: University
\nof Michigan Press 1975.<\/p>\n
[nordin]
\nPeter Nordin, Frank Francone, and Wolfgang Banzhaf, 1996, Explicitly defined introns and destructive
\ncrossover in genetic programming, Advances in Genetic Programming 2, chapter 6, pp. 111\u2013134, MIT
\nPress, Cambridge,MA, USA.<\/p>\n
[blprnt] www.blprnt.com\/variance\/<\/p>\n","protected":false},"excerpt":{"rendered":"
To what extent does imitating biological evolution benefit genetic programming? Genetic algorithms are a set of search algorithms that have been inspired by biological evolution [holland]. They have been used in applications from *** to creative logo design[blprnt], by using variations of biological mutation, fitness-based selection, and populations. Over the years, the benefits provided by […]<\/p>\n","protected":false},"author":1187,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[7349,7350],"tags":[],"class_list":["post-202","post","type-post","status-publish","format-standard","hentry","category-murc","category-murc-proposals"],"_links":{"self":[{"href":"https:\/\/blogs.ubc.ca\/evolsds\/wp-json\/wp\/v2\/posts\/202","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/blogs.ubc.ca\/evolsds\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/blogs.ubc.ca\/evolsds\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/blogs.ubc.ca\/evolsds\/wp-json\/wp\/v2\/users\/1187"}],"replies":[{"embeddable":true,"href":"https:\/\/blogs.ubc.ca\/evolsds\/wp-json\/wp\/v2\/comments?post=202"}],"version-history":[{"count":1,"href":"https:\/\/blogs.ubc.ca\/evolsds\/wp-json\/wp\/v2\/posts\/202\/revisions"}],"predecessor-version":[{"id":203,"href":"https:\/\/blogs.ubc.ca\/evolsds\/wp-json\/wp\/v2\/posts\/202\/revisions\/203"}],"wp:attachment":[{"href":"https:\/\/blogs.ubc.ca\/evolsds\/wp-json\/wp\/v2\/media?parent=202"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/blogs.ubc.ca\/evolsds\/wp-json\/wp\/v2\/categories?post=202"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/blogs.ubc.ca\/evolsds\/wp-json\/wp\/v2\/tags?post=202"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}