The Ant Swarm Neuro-Evolution Procedure for Optimizing Recurrent Networks

TL;DR

This paper introduces ASNE, a novel ant colony optimization-based neuro-evolution method for designing sparse, high-performing recurrent neural networks with various cell types, outperforming existing architectures and algorithms.

Contribution

The paper presents a new neuro-evolution algorithm using ACO for optimizing RNN topologies, incorporating specialized ant roles, regularization-inspired pheromone functions, and a Lamarckian weight initialization strategy.

Findings

Evolved RNNs outperform traditional architectures.

ASNE achieves state-of-the-art results on time series data.

Sparser RNNs maintain high performance with fewer parameters.

Abstract

Hand-crafting effective and efficient structures for recurrent neural networks (RNNs) is a difficult, expensive, and time-consuming process. To address this challenge, we propose a novel neuro-evolution algorithm based on ant colony optimization (ACO), called ant swarm neuro-evolution (ASNE), for directly optimizing RNN topologies. The procedure selects from multiple modern recurrent cell types such as Delta-RNN, GRU, LSTM, MGU and UGRNN cells, as well as recurrent connections which may span multiple layers and/or steps of time. In order to introduce an inductive bias that encourages the formation of sparser synaptic connectivity patterns, we investigate several variations of the core algorithm. We do so primarily by formulating different functions that drive the underlying pheromone simulation process (which mimic L1 and L2 regularization in standard machine learning) as well as by introducing ant agents with specialized roles (inspired by how real ant colonies operate), i.e., explorer ants that construct the initial feed forward structure and social ants which select nodes from the feed forward connections to subsequently craft recurrent memory structures. We also incorporate a Lamarckian strategy for weight initialization which reduces the number of backpropagation epochs required to locally train candidate RNNs, speeding up the neuro-evolution process. Our results demonstrate that the sparser RNNs evolved by ASNE significantly outperform traditional one and two layer architectures consisting of modern memory cells, as well as the well-known NEAT algorithm. Furthermore, we improve upon prior state-of-the-art results on the time series dataset utilized in our experiments.