Understanding Nonmodular Functionality: Lessons from Genetic Algorithms.

Evolution is often characterized as a tinkerer creating efficient but messy solutions. We analyze the nature of the problems that arise when trying to explain and understand cognitive phenomena created by this haphazard design process. We present a theory of explanation and understanding and apply i...

Descripción completa

Detalles Bibliográficos
Publicado en:Philosophy of Science Vol. 80; no. 5; pp. 637 - 650
Autores principales: Kuorikoski, Jaakko, Pöyhönen, Samuli
Formato: Artículo
Publicado: Cambridge University Press Dec2013
Materias:
Acceso en línea:Ver este registro en EBSCOhost
fields @attributes:
  recordID: 1
pdfLink:
plink: https://search.ebscohost.com/login.aspx?direct=true&db=hlh&AN=93642184&site=ehost-live
header:
  @attributes:
    shortDbName: hlh
    uiTerm: 93642184
    longDbName: Humanities International Complete
    uiTag: AN
  controlInfo:
    bkinfo:
    jinfo:
      jid:
        00318248
        PSC
      jtl: Philosophy of Science
      issn: 00318248
      maglogo: N
    pubinfo:
      dt: Dec2013
      vid: 80
      iid: 5
      pid: 15979
      pub: Cambridge University Press
    artinfo:
      ui:
        93642184
        10.1086/673866
      ppf: 637
      ppct: 13
      formats:
        fmt:
          – @attributes:
              type: T
          – @attributes:
              type: P
              size: 370KB
      tig:
        atl: Understanding Nonmodular Functionality: Lessons from Genetic Algorithms.
      aug:
        au:
          Kuorikoski, Jaakko
          Pöyhönen, Samuli
      su:
        Genetic algorithms
        Biological evolution
        Convenience sampling (Statistics)
        Cognitive science
        Evolutionary algorithms
      sug:
        subj:
          Genetic algorithms
          Biological evolution
          Convenience sampling (Statistics)
          Cognitive science
          Evolutionary algorithms
      ab: Evolution is often characterized as a tinkerer creating efficient but messy solutions. We analyze the nature of the problems that arise when trying to explain and understand cognitive phenomena created by this haphazard design process. We present a theory of explanation and understanding and apply it to a case problem--solutions generated by genetic algorithms. By analyzing the nature of solutions that genetic algorithms present to computational problems, we show, first, that evolutionary designs are often hard to understand because they exhibit nonmodular functionality and, second, that breaches of modularity wreak havoc on our strategies of causal and constitutive explanation.
      pubtype: Academic Journal
      doctype: Article
      src: R
    language: English
    refInfo:
    copyright:
      @attributes:
        flag: Y
      custom: Copyright of Philosophy of Science is the property of Cambridge University Press and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use.
      item: Philosophy of Science
      holder: Cambridge University Press
      dt:
        @attributes:
          year: 2013
    holdings:
      @attributes:
        islocal: N