About: Stepwise Evolutionary Learning using Deep Learned Guidance Functions

Not logged in : Login

(Sponging disallowed)

Facets (new session)
Description
Metadata
Settings
- Rule:
- Inverse Functional Properties:
- "Same As":

About: Stepwise Evolutionary Learning using Deep Learned Guidance Functions Goto Sponge NotDistinct Permalink

An Entity of Type : bibo:AcademicArticle, within Data Space : linkeddata.uriburner.com:28898 associated with source document(s)

Attributes	Values
type	http://eprints.org/ontology/ConferenceItemEPrint http://eprints.org/ontology/EPrint Article Academic Article
seeAlso	HTML Summary of #78198 Stepwise Evolutionary Learning using Deep Learned Guidance Functions
sameAs	Stepwise Evolutionary Learning using Deep Learned Guidance Functions
http://www.loc.gov...erms/relators/EDT	Miltos Petridis Max Bramer
http://eprints.org/ontology/hasAccepted	Stepwise Evolutionary Learning using Deep Learned Guidance Functions (PDF)
http://eprints.org/ontology/hasDocument	Stepwise Evolutionary Learning using Deep Learned Guidance Functions (PDF) Stepwise Evolutionary Learning using Deep Learned Guidance Functions (Other) Stepwise Evolutionary Learning using Deep Learned Guidance Functions (Other) Stepwise Evolutionary Learning using Deep Learned Guidance Functions (Other) Stepwise Evolutionary Learning using Deep Learned Guidance Functions (Other) Stepwise Evolutionary Learning using Deep Learned Guidance Functions (Other)
dc:hasVersion	Stepwise Evolutionary Learning using Deep Learned Guidance Functions (PDF)
Title	Stepwise Evolutionary Learning using Deep Learned Guidance Functions
described by	https://demo.openlinksw.com/about/id/entity/https/www.cs.kent.ac.uk/people/staff/akj22/materials/CO644/export_kar_RDFN3.n3 https://demo.openlinksw.com/about/id/entity/https/raw.githubusercontent.com/annajordanous/CO644Files/main/export_kar_RDFN3.n3
Date	2019-11-19
Creator	Colin G. Johnson
status	peer reviewed published
Publisher	Springer
abstract	This paper explores how Learned Guidance Functions (LGFs)— a pre-training method used to smooth search landscapes—can be used as a fitness function for evolutionary algorithms. A new form of LGF is introduced, based on deep neural network learning, and it is shown how this can be used as a fitness function. This is applied to a test problem: unscrambling the Rubik’s Cube. Comparisons are made with a previous LGF approach based on random forests, and with a baseline approach based on traditional error-based fitness.
Is Part Of	Financial Cryptography and Data Security https://kar.kent.ac.uk/id/repository
Subject	QA76.87 Neural computers, neural networks
list of authors	https://kar.kent.ac.uk/id/eprint/78198#authors
list of editors	https://kar.kent.ac.uk/id/eprint/78198#editors
presented at	39th SGAI International Conference on Artificial Intelligence, AI 2019
volume	11927
is topic of	https://www.cs.kent.ac.uk/people/staff/akj22/materials/CO644/export_kar_RDFN3.n3 https://raw.githubusercontent.com/annajordanous/CO644Files/main/export_kar_RDFN3.n3
is primary topic of	HTML Summary of #78198 Stepwise Evolutionary Learning using Deep Learned Guidance Functions

Faceted Search & Find service v1.17_git144 as of Jul 26 2024

Alternative Linked Data Documents: iSPARQL | ODE Content Formats:

RDF

ODATA

Microdata

About

OpenLink Virtuoso version 08.03.3331 as of Aug 25 2024, on Linux (x86_64-ubuntu_noble-linux-glibc2.38-64), Single-Server Edition (378 GB total memory, 38 GB memory in use)
Data on this page belongs to its respective rights holders.
Virtuoso Faceted Browser Copyright © 2009-2024 OpenLink Software