Chaotic Map-Coded Evolutionary Algorithms for Dendritic Neuron Model Optimization

In the domain of artificial neural networks, the comprehensive understanding and optimization of neuron dynamics are crucial. The Dendritic Neuron Model (DNM), noted for its distinct architecture and data processing capabilities, exemplifies this. However, the sophistication of the DNM leads to complexities in hyperparameter tuning. Notably, this complexity manifests in the way parameter changes can alter the dimensions of the solution space, a prime example of the Metameric Variable-length Problem. In this study, we have innovatively integrated chaotic maps into the gene expression mechanisms of Evolutionary Algorithms, enabling the incorporation of all DNM hyperparameters into a singular algorithmic framework. This integration allows for iterative adjustments within a variable-dimensional solution space, representing an evolving neuron model that streamlines the tuning process. Our approach, tested against benchmark datasets from the UCI Machine Learning Repository, demonstrates significant improvements in the DNM's performance, highlighting the effectiveness of incorporating biological chaos phenomena into neural network optimization.