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Information-theoretic neuro-correlates boost evolution of cognitive systems
Michigan State University, East Lansing, United States.ORCID iD: 0000-0002-4872-1961
2016 (English)In: Entropy, E-ISSN 1099-4300, Vol. 18, no 1, article id 6Article in journal (Refereed) Published
Abstract [en]

Genetic Algorithms (GA) are a powerful set of tools for search and optimization that mimic the process of natural selection, and have been used successfully in a wide variety of problems, including evolving neural networks to solve cognitive tasks. Despite their success, GAs sometimes fail to locate the highest peaks of the fitness landscape, in particular if the landscape is rugged and contains multiple peaks. Reaching distant and higher peaks is difficult because valleys need to be crossed, in a process that (at least temporarily) runs against the fitness maximization objective. Here we propose and test a number of information-theoretic (as well as network-based) measures that can be used in conjunction with a fitness maximization objective (so-called "neuro-correlates") to evolve neural controllers for two widely different tasks: A behavioral task that requires information integration, and a cognitive task that requires memory and logic. We find that judiciously chosen neuro-correlates can significantly aid GAs to find the highest peaks. © 2015 by the authors.

Place, publisher, year, edition, pages
MDPI AG , 2016. Vol. 18, no 1, article id 6
Keywords [en]
Evolution, Genetic algorithm, Information theory, Markov brain, Neuro-correlate
National Category
Evolutionary Biology
Identifiers
URN: urn:nbn:se:du-37179DOI: 10.3390/e18010006Scopus ID: 2-s2.0-84956694510OAI: oai:DiVA.org:du-37179DiVA, id: diva2:1557883
Available from: 2021-05-27 Created: 2021-05-27 Last updated: 2023-03-28Bibliographically approved

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Hintze, Arend

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CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • chicago-author-date
  • chicago-note-bibliography
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf