Evolutive Layers in M3GP Basis for Symbolic Deep Learning Models

Abstract

This paper introduces a novel approach toconstructive induction in genetic programming throughthe Multidimensional Multiclass Genetic Programmingwith Multidimensional populations also known as M3GPalgorithm. M3GP is leveraged to create new featuresthat either augment the original dataset or transform itinto a refined version, resulting in improved performancefor learning algorithms. With this premise the primarycontribution of this work is the integration of an evolutivelayer structure within M3GP, where the n best-performingfeatures generated in the previous iterations are reusedto continuously enhance the algorithm’s performance.This approach parallels the concept of layers inneural networks, establishing a pathway for symbolicconstruction methods, such as genetic programming, toincorporate layered learning. The second contribution isdefining the structure of operation that can be appliedin any constructive induction method to connect theimprovement of symbolic models, and sampling keypoints of improvement for configuration options. Thefindings underscore the potential of evolutionary layeringto improve feature generation and model accuracy,marking an advancement in the constructive inductionfield into a deep learning process. The result showsa higher tendency of fitness improvement againstnon-layered networks for regression problems and alower improvement in classification problems, openingthe possibilities for a new niche for deep evolutivenetworks.