NDLI: Improving Neuroevolution with Complementarity-Based Selection Operators

Content Provider	Springer Nature Link
Author	Maul, Tomás H.
Copyright Year	2016
Abstract	This paper is concerned with the problem of improving the convergence properties of evolutionary neural networks, particularly in the context of hybrid neural networks that adopt a diversity of transfer functions at their nodes, i.e.: neural diversity machines. The paper explores the potential of solution complementarity, in the context of pattern recognition problems, and focuses on its incorporation in the selection process of recombination heuristics. In a pattern recognition context, complementarity is defined as the ability of different solutions to correctly classify complementary subsets of patterns. A broad set of experiments was conducted demonstrating that solution selection based on complementarity is statistically significantly better than random selection, in a wide range of conditions, e.g.: different datasets, recombination heuristics and architectural constraints. Although the experiments demonstrated the statistical significance and robustness of the effect, they also indicated that more work is required to increase the degree of the effect and to scale-up to larger and more complex datasets.
Starting Page	887
Ending Page	911
Page Count	25
File Format	PDF
ISSN	13704621
Journal	Neural Processing Letters
Volume Number	44
Issue Number	3
e-ISSN	1573773X
Language	English
Publisher	Springer US
Publisher Date	2016-01-13
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Neuroevolution Evolutionary neural networks Neural diversity machines Recombination heuristics Pattern recognition Machine learning Artificial Intelligence (incl. Robotics) Complex Systems Computational Intelligence
Content Type	Text
Resource Type	Article
Subject	Neuroscience Artificial Intelligence Computer Networks and Communications Software

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