Distance Measures for HyperGP with Fitness Sharing
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F12%3A00195563" target="_blank" >RIV/68407700:21230/12:00195563 - isvavai.cz</a>
Result on the web
<a href="http://dl.acm.org/citation.cfm?id=2330241" target="_blank" >http://dl.acm.org/citation.cfm?id=2330241</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1145/2330163.2330241" target="_blank" >10.1145/2330163.2330241</a>
Alternative languages
Result language
angličtina
Original language name
Distance Measures for HyperGP with Fitness Sharing
Original language description
In this paper we propose a new algorithm called HyperGPEFS (HyperGP with Explicit Fitness Sharing). It is based on a HyperNEAT, which is a well-established evolutionary method employing indirect encoding of artificial neural networks. Indirect encoding in HyperNEAT is realized via special function called Compositional and Pattern Producing Network (CPPN), able to describe a neural network of arbitrary size. CPPNs are represented by network structures, which are evolved by means of a slightly modified version of another, well-known algorithm NEAT (NeuroEvolution of Augmenting Topologies). HyperGP is a variant of HyperNEAT, where the CPPNs are optimized by Genetic Programming (GP). Published results reported promising improvement in the speed of convergence. Our approach further extends HyperGP by using fitness sharing to promote a diversity of a population. Here, we thoroughly compare all three algorithms on six different tasks. Fitness sharing demands a definition of a tree distance me
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
IN - Informatics
OECD FORD branch
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Result continuities
Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2012
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Proceedings of the fourteenth international conference on Genetic and evolutionary computation conference companion
ISBN
978-1-4503-1177-9
ISSN
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e-ISSN
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Number of pages
8
Pages from-to
545-552
Publisher name
ACM
Place of publication
New York
Event location
Philadelphia
Event date
Jul 7, 2012
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000309611100069