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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

  • Czech description

Classification

  • Type

    D - Article in proceedings

  • CEP classification

    IN - Informatics

  • OECD FORD branch

Result continuities

  • Project

  • 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

  • e-ISSN

  • 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