This HTML5 document contains 47 embedded RDF statements represented using HTML+Microdata notation.

The embedded RDF content will be recognized by any processor of HTML5 Microdata.

Namespace Prefixes

PrefixIRI
dctermshttp://purl.org/dc/terms/
n7https://web.archive.org/web/20171223102607/https:/pdfs.semanticscholar.org/7e95/
yago-reshttp://yago-knowledge.org/resource/
dbohttp://dbpedia.org/ontology/
foafhttp://xmlns.com/foaf/0.1/
n21http://www.tim-taylor.com/papers/
n15https://global.dbpedia.org/id/
n6http://www.envplan.com/
yagohttp://dbpedia.org/class/yago/
dbthttp://dbpedia.org/resource/Template:
rdfshttp://www.w3.org/2000/01/rdf-schema#
n25https://web.archive.org/web/20110719123923/http:/www.ece.stevens-tech.edu/~ymeng/publications/
freebasehttp://rdf.freebase.com/ns/
n24https://web.archive.org/web/20110629022041/http:/mitpress.mit.edu/books/chapters/
rdfhttp://www.w3.org/1999/02/22-rdf-syntax-ns#
owlhttp://www.w3.org/2002/07/owl#
wikipedia-enhttp://en.wikipedia.org/wiki/
dbphttp://dbpedia.org/property/
provhttp://www.w3.org/ns/prov#
dbchttp://dbpedia.org/resource/Category:
xsdhhttp://www.w3.org/2001/XMLSchema#
wikidatahttp://www.wikidata.org/entity/
goldhttp://purl.org/linguistics/gold/
n22http://panmental.de/
n23https://www.researchgate.net/profile/James_Whitacre/publication/220701596_The_Role_of_Degenerate_Robustness_in_the_Evolvability_of_Multi-agent_Systems_in_Dynamic_Environments/links/
dbrhttp://dbpedia.org/resource/
n8https://web.archive.org/web/20171223102623/https:/pdfs.semanticscholar.org/1846/

Statements

Subject Item
dbr:Artificial_development
rdf:type
yago:Algorithm105847438 yago:Abstraction100002137 dbo:Place yago:Act100030358 yago:WikicatEvolutionaryAlgorithms yago:Activity100407535 yago:PsychologicalFeature100023100 yago:Rule105846932 yago:YagoPermanentlyLocatedEntity yago:Procedure101023820 yago:Event100029378
rdfs:label
Artificial development
rdfs:comment
Artificial development, also known as artificial embryogeny or machine intelligence or computational development, is an area of computer science and engineering concerned with computational models motivated by genotype–phenotype mappings in biological systems. Artificial development is often considered a sub-field of evolutionary computation, although the principles of artificial development have also been used within stand-alone computational models.
dcterms:subject
dbc:Evolutionary_algorithms
dbo:wikiPageID
16132124
dbo:wikiPageRevisionID
1050669751
dbo:wikiPageWikiLink
dbr:Morphogen dbr:Artificial_Life_(journal) dbr:Evolutionary_computation dbr:Degeneracy_(biology) dbr:Parallel_Problem_Solving_from_Nature dbr:Grammatical_evolution dbr:Cellular_differentiation dbr:Cell_division dbr:Engineering dbc:Evolutionary_algorithms dbr:Gene_regulatory_networks dbr:Computer_science
dbo:wikiPageExternalLink
n6:abstract.cgi%3Fid=b3015 n7:e1b5964bae64046d7c1ecdef8c2f43a1b469.pdf n8:d622d1ea438f1e8c0acab27a90f415e018a1.pdf n21:taylor_grn.pdf n22:ALifeXIflag n23:0d2b2c6889b5121d730dd3be.pdf) n24:0262287196chap42.pdf n25:BioSystems09_Meng.pdf)
owl:sameAs
wikidata:Q4801064 n15:4RwS9 yago-res:Artificial_development freebase:m.03w9_gw
dbp:wikiPageUsesTemplate
dbt:ISBN dbt:Evolutionary_algorithms
dbo:abstract
Artificial development, also known as artificial embryogeny or machine intelligence or computational development, is an area of computer science and engineering concerned with computational models motivated by genotype–phenotype mappings in biological systems. Artificial development is often considered a sub-field of evolutionary computation, although the principles of artificial development have also been used within stand-alone computational models. Within evolutionary computation, the need for artificial development techniques was motivated by the perceived lack of scalability and evolvability of direct solution encodings (Tufte, 2008). Artificial development entails indirect solution encoding. Rather than describing a solution directly, an indirect encoding describes (either explicitly or implicitly) the process by which a solution is constructed. Often, but not always, these indirect encodings are based upon biological principles of development such as morphogen gradients, cell division and cellular differentiation (e.g. Doursat 2008), gene regulatory networks (e.g. Guo et al., 2009), degeneracy (Whitacre et al., 2010), grammatical evolution (de Salabert et al., 2006), or analogous computational processes such as re-writing, iteration, and time. The influences of interaction with the environment, spatiality and physical constraints on differentiated multi-cellular development have been investigated more recently (e.g. Knabe et al. 2008). Artificial development approaches have been applied to a number of computational and design problems, including electronic circuit design (Miller and Banzhaf 2003), robotic controllers (e.g. Taylor 2004), and the design of physical structures (e.g. Hornby 2004).
gold:hypernym
dbr:Area
prov:wasDerivedFrom
wikipedia-en:Artificial_development?oldid=1050669751&ns=0
dbo:wikiPageLength
5040
foaf:isPrimaryTopicOf
wikipedia-en:Artificial_development