Improving Neuroevolution with Complementarity-Based Selection Operators

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.