deforce: Derivative-free algorithms for optimizing Cascade Forward Neural Networks

Abstract

This paper aims to introduce the ‘deforce’ framework, an open-source Python library constituted on top of Numpy, Scikit-Learn, PyTorch, and Mealpy. This framework provides hybrid models that combine derivative-free techniques with Cascade Forward Neural Networks (CFNNs). By inheriting from scikit-learn’s estimator, deforce’s models ensure easy integration into existing machine learning pipelines. It also has many advantages, including a simple installation process, a user-friendly interface, and adaptability to various user requirements. For researchers and practitioners looking to improve CFNN performance with minimal implementation effort, deforce offers a useful and approachable option.