Improving Neuroevolution with Complementarity-Based Selection Operators

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• 作者：Tomás H. Maul
• 关键词：Neuroevolution ; Evolutionary neural networks ; Neural diversity machines ; Recombination heuristics ; Pattern recognition ; Machine learning
• 刊名：Neural Processing Letters
• 出版年：2016
• 出版时间：December 2016
• 年：2016
• 卷：44
• 期：3
• 页码：887-911
• 全文大小：1,623 KB
• 刊物类别：Physics and Astronomy
• 刊物主题：Physics
  Complexity
  Artificial Intelligence and Robotics
  Electronic and Computer Engineering
  Operation Research and Decision Theory
  
• 出版者：Springer Netherlands
• ISSN：1573-773X
• 卷排序：44

文摘

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.