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Entropy 2016, 18(1), 6; doi:10.3390/e18010006

Information-Theoretic Neuro-Correlates Boost Evolution of Cognitive Systems

1
Department of Computer Science and Engineering, Michigan State University, East Lansing, MI 48824, USA
2
BEACON Center for the Study of Evolution in Action, Michigan State University, East Lansing, MI 48824, USA
3
Department of Microbiology & Molecular Genetics, Michigan State University, East Lansing, MI 48824, USA
4
Department of Integrative Biology, Michigan State University, East Lansing, MI 48824, USA
*
Author to whom correspondence should be addressed.
Academic Editors: Christoph Salge, Georg Martius, Keyan Ghazi-Zahedi and Daniel Polani
Received: 1 July 2015 / Revised: 9 December 2015 / Accepted: 22 December 2015 / Published: 25 December 2015
(This article belongs to the Special Issue Information Theoretic Incentives for Cognitive Systems)
View Full-Text   |   Download PDF [4492 KB, uploaded 25 December 2015]   |  

Abstract

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. View Full-Text
Keywords: genetic algorithm; neuro-correlate; markov brain; evolution; information theory genetic algorithm; neuro-correlate; markov brain; evolution; information theory
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Schossau, J.; Adami, C.; Hintze, A. Information-Theoretic Neuro-Correlates Boost Evolution of Cognitive Systems. Entropy 2016, 18, 6.

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