Hill Climb Modular Assembler Encoding: Evolving Modular Neural Networks of fixed modular architecture

The paper presents a novel generative Neuro-Evolutionary (NE) method called Hill Climb Modular Assembler Encoding (HCMAE). The target application of the HCMAE is to evolve modular Artificial Neural Networks (ANNs) whose modular structure is known in advance. Different variants of HCMAE were tested on two well-known ANN benchmarks, i.e. the Two-Spiral problem (feed-forward ANNs), and the Inverted-Pendulum problem (recurrent ANNs), for four different modular neural architectures. Particle Swarm Optimization and Differential Evolution were selected as rivals for HCMAE. Both rival methods were tested in two variants, i.e. a classical one-population variant and cooperative co-evolutionary multi-population variant. The paper presents the proposed method and reports all the experiments. (C) 2021 Elsevier B.V. All rights reserved.

Automated summary

This paper introduces a novel generative Neuro-Evolutionary method called Hill Climb Modular Assembler Encoding (HCMAE) for evolving modular Artificial Neural Networks (ANNs). By testing different variants on two ANN benchmarks, the effectiveness of the HCMAE method is demonstrated.