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dc.contributor.authorEgri-Nagy, Attila
dc.contributor.authorNehaniv, C.L.
dc.date.accessioned2011-08-15T11:01:52Z
dc.date.available2011-08-15T11:01:52Z
dc.date.issued2003
dc.identifier.citationEgri-Nagy , A & Nehaniv , C L 2003 , ' Evolvability of the gentotype-phenotype relation in populations of self-replicating digital organisms in a Tierra-like system ' , Lecture Notes in Computer Science (LNCS) , vol. 2801 , pp. 238-247 . https://doi.org/10.1007/978-3-540-39432-7_26
dc.identifier.issn0302-9743
dc.identifier.urihttp://hdl.handle.net/2299/6205
dc.description"The original publication is available at www.springerlink.com" Copyright Springer
dc.description.abstractIn other Tierra-like systems the genotype is a sequence of instructions and the phenotype is the corresponding executed algorithm. This way the genotype-phenotype mapping is constrained by the structure of a creature’s processor, and this structure was fixed for an evolutionary scenario in previous systems. Our approach here is to put the mapping under evolutionary control. We use a universal processor (analogous to a universal Turing-machine) and put the structural description of the creature’s processor as well as the instruction set of the actual processor into the organism’s genome. The life-cycle of an organism begins with building its actual processor, then the organism can start executing instructions in the rest of its genome with the newly built processor. Since the definitions of the processors and instruction sets are in the genome, they are subject to mutations and heritable variation enabling their evolution. In this work we investigate the evolutionary development of the processor structures. In evolving populations, changes in the components (registers, stacks, queues), variations in instruction-set size and the redefinition of the instructions can be observed during experiments.en
dc.format.extent220139
dc.language.isoeng
dc.relation.ispartofLecture Notes in Computer Science (LNCS)
dc.titleEvolvability of the gentotype-phenotype relation in populations of self-replicating digital organisms in a Tierra-like systemen
dc.contributor.institutionSchool of Computer Science
dc.contributor.institutionScience & Technology Research Institute
dc.description.statusPeer reviewed
rioxxterms.versionofrecord10.1007/978-3-540-39432-7_26
rioxxterms.typeJournal Article/Review
herts.preservation.rarelyaccessedtrue


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