PUB - Publikationen an der Universität Bielefeld

Generating machine learning models is inherently a multi-objective optimization problem. Two most common objectives are accuracy and interpretability, which are very likely conflicting with each other. While in most cases we are interested only in the model accuracy, interpretability of the model becomes the major concern if the model is used for data mining or if the model is applied to critical applications. In this chapter, we present a method for simultaneously generating accurate and interpretable neural network models for classification using an evolutionary multi-objective optimization algorithm. Lifetime learning is embedded to fine-tune the weights in the evolution that mutates the structure and weights of the neural networks. The efficiency of Baldwin effect and Lamarckian evolution are compared. It is found that the Lamarckian evolution outperforms the Baldwin effect in evolutionary multi-objective optimization of neural networks. Simulation results on two benchmark problems demonstrate that the evolutionary multi-objective approach is able to generate both accurate and understandable neural network models, which can be used for different purpose.