Understanding evolutionary potential in virtual CPU instruction set architectures.

We investigate fundamental decisions in the design of instruction set architectures for linear genetic programs that are used as both model systems in evolutionary biology and underlying solution representations in evolutionary computation. We subjected digital organisms with each tested architectur...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	David M Bryson, Charles Ofria
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2013
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/a70a432e766e45ae83a8dcf4fb3c3431
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:a70a432e766e45ae83a8dcf4fb3c3431
record_format	dspace
spelling	oai:doaj.org-article:a70a432e766e45ae83a8dcf4fb3c34312021-11-18T08:40:37ZUnderstanding evolutionary potential in virtual CPU instruction set architectures.1932-620310.1371/journal.pone.0083242https://doaj.org/article/a70a432e766e45ae83a8dcf4fb3c34312013-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/24376669/?tool=EBIhttps://doaj.org/toc/1932-6203We investigate fundamental decisions in the design of instruction set architectures for linear genetic programs that are used as both model systems in evolutionary biology and underlying solution representations in evolutionary computation. We subjected digital organisms with each tested architecture to seven different computational environments designed to present a range of evolutionary challenges. Our goal was to engineer a general purpose architecture that would be effective under a broad range of evolutionary conditions. We evaluated six different types of architectural features for the virtual CPUs: (1) genetic flexibility: we allowed digital organisms to more precisely modify the function of genetic instructions, (2) memory: we provided an increased number of registers in the virtual CPUs, (3) decoupled sensors and actuators: we separated input and output operations to enable greater control over data flow. We also tested a variety of methods to regulate expression: (4) explicit labels that allow programs to dynamically refer to specific genome positions, (5) position-relative search instructions, and (6) multiple new flow control instructions, including conditionals and jumps. Each of these features also adds complication to the instruction set and risks slowing evolution due to epistatic interactions. Two features (multiple argument specification and separated I/O) demonstrated substantial improvements in the majority of test environments, along with versions of each of the remaining architecture modifications that show significant improvements in multiple environments. However, some tested modifications were detrimental, though most exhibit no systematic effects on evolutionary potential, highlighting the robustness of digital evolution. Combined, these observations enhance our understanding of how instruction architecture impacts evolutionary potential, enabling the creation of architectures that support more rapid evolution of complex solutions to a broad range of challenges.David M BrysonCharles OfriaPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 8, Iss 12, p e83242 (2013)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q David M Bryson Charles Ofria Understanding evolutionary potential in virtual CPU instruction set architectures.
description	We investigate fundamental decisions in the design of instruction set architectures for linear genetic programs that are used as both model systems in evolutionary biology and underlying solution representations in evolutionary computation. We subjected digital organisms with each tested architecture to seven different computational environments designed to present a range of evolutionary challenges. Our goal was to engineer a general purpose architecture that would be effective under a broad range of evolutionary conditions. We evaluated six different types of architectural features for the virtual CPUs: (1) genetic flexibility: we allowed digital organisms to more precisely modify the function of genetic instructions, (2) memory: we provided an increased number of registers in the virtual CPUs, (3) decoupled sensors and actuators: we separated input and output operations to enable greater control over data flow. We also tested a variety of methods to regulate expression: (4) explicit labels that allow programs to dynamically refer to specific genome positions, (5) position-relative search instructions, and (6) multiple new flow control instructions, including conditionals and jumps. Each of these features also adds complication to the instruction set and risks slowing evolution due to epistatic interactions. Two features (multiple argument specification and separated I/O) demonstrated substantial improvements in the majority of test environments, along with versions of each of the remaining architecture modifications that show significant improvements in multiple environments. However, some tested modifications were detrimental, though most exhibit no systematic effects on evolutionary potential, highlighting the robustness of digital evolution. Combined, these observations enhance our understanding of how instruction architecture impacts evolutionary potential, enabling the creation of architectures that support more rapid evolution of complex solutions to a broad range of challenges.
format	article
author	David M Bryson Charles Ofria
author_facet	David M Bryson Charles Ofria
author_sort	David M Bryson
title	Understanding evolutionary potential in virtual CPU instruction set architectures.
title_short	Understanding evolutionary potential in virtual CPU instruction set architectures.
title_full	Understanding evolutionary potential in virtual CPU instruction set architectures.
title_fullStr	Understanding evolutionary potential in virtual CPU instruction set architectures.
title_full_unstemmed	Understanding evolutionary potential in virtual CPU instruction set architectures.
title_sort	understanding evolutionary potential in virtual cpu instruction set architectures.
publisher	Public Library of Science (PLoS)
publishDate	2013
url	https://doaj.org/article/a70a432e766e45ae83a8dcf4fb3c3431
work_keys_str_mv	AT davidmbryson understandingevolutionarypotentialinvirtualcpuinstructionsetarchitectures AT charlesofria understandingevolutionarypotentialinvirtualcpuinstructionsetarchitectures
_version_	1718421462721757184

Understanding evolutionary potential in virtual CPU instruction set architectures.

Ejemplares similares