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Genetic Algorithm for Automatic tuning of neural network hyperparameters

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F63839172%3A_____%2F18%3A10133127" target="_blank" >RIV/63839172:_____/18:10133127 - isvavai.cz</a>

  • Result on the web

    <a href="http://dx.doi.org/10.1117/12.2304955" target="_blank" >http://dx.doi.org/10.1117/12.2304955</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.1117/12.2304955" target="_blank" >10.1117/12.2304955</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    Genetic Algorithm for Automatic tuning of neural network hyperparameters

  • Original language description

    Articial neural networks affect our everyday life. But every neural network depends on the appropriate training set and setting of internal properties with hyperparameters. Even accurate and complete training set doesnt imply high performance of neural network algorithm. Tuning of hyperparameters is crucial for correct functionality, fast learning and high accuracy of neural networks. The hyperparameter selection relies on manual ne-tuning based on multiple full training trials. There are a lot of neural network implementation available for public and commercial use, but the setting of hyperparameters is often a neglected problem. Choosing the best structure and hyperparameters is the primary challenge in designing a neural network. This article describes a genetic algorithm for automatic selection of hyperparameters and their tuning for increasing the performance of neural networks without human interaction. The optimization algorithm accelerates the discovery of conguration, which is otherwise a time-consuming task. We evaluate the results of optimizations in comparison to naive approach and compare pro and cons of different techniques.

  • Czech name

  • Czech description

Classification

  • Type

    D - Article in proceedings

  • CEP classification

  • OECD FORD branch

    10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Result continuities

  • Project

    <a href="/en/project/EF16_013%2F0001797" target="_blank" >EF16_013/0001797: CESNET E-Infrastructure - Modernisation</a><br>

  • Continuities

    P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Others

  • Publication year

    2018

  • Confidentiality

    S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Data specific for result type

  • Article name in the collection

    Autonomous Systems: Sensors, Vehicles, Security, and the Internet of Everything

  • ISBN

    978-1-5106-1798-8

  • ISSN

    0277-786X

  • e-ISSN

    neuvedeno

  • Number of pages

    7

  • Pages from-to

  • Publisher name

    SPIE

  • Place of publication

    Neuveden

  • Event location

    Orlando, Florida, United States

  • Event date

    Apr 16, 2018

  • Type of event by nationality

    WRD - Celosvětová akce

  • UT code for WoS article

    000453556800017