Hybrid Self-Attention NEAT: A novel evolutionary approach to improve the NEAT algorithm

Abstract

This article presents a "Hybrid Self-Attention NEAT" method to improve the original NeuroEvolution of Augmenting Topologies (NEAT) algorithm in high-dimensional inputs. Although the NEAT algorithm has shown a significant result in different challenging tasks, as input representations are high dimensional, it cannot create a well-tuned network. Our study addresses this limitation by using self-attention as an indirect encoding method to select the most important parts of the input. In addition, we improve its overall performance with the help of a hybrid method to evolve the final network weights. The main conclusion is that Hybrid Self- Attention NEAT can eliminate the restriction of the original NEAT. The results indicate that in comparison with evolutionary algorithms, our model can get comparable scores in Atari games with raw pixels input with a much lower number of parameters.

References in corpus (9)

• Deep Convolutional Neural Network for Inverse Problems in Imaging
• AutoML: A Survey of the State-of-the-Art
• A Deep Reinforced Model for Abstractive Summarization
• The Arcade Learning Environment: An Evaluation Platform for General Agents
• Evolution Strategies as a Scalable Alternative to Reinforcement Learning
• Deep Neuroevolution: Genetic Algorithms Are a Competitive Alternative for Training Deep Neural Networks for Reinforcement Learning
• A Deep-Learning Intelligent System Incorporating Data Augmentation for Short-Term Voltage Stability Assessment of Power Systems
• Application of EOS-ELM with binary Jaya-based feature selection to real-time transient stability assessment using PMU data
• Neuroevolution of Self-Interpretable Agents