Recombination and Novelty in Neuroevolution: A Visual Analysis

Citation

Sarti S, Adair J & Ochoa G (2022) Recombination and Novelty in Neuroevolution: A Visual Analysis. SN Computer Science, 3 (3), Art. No.: 185. https://doi.org/10.1007/s42979-022-01064-6

Abstract
Neuroevolution has re-emerged as an active topic in the last few years. However, there is a lack of accessible tools to analyse, contrast and visualise the behaviour of neuroevolution systems. A variety of search strategies have been proposed such as Novelty search and Quality-Diversity search, but their impact on the evolutionary dynamics is not well understood. We propose using a data-driven, graph-based model, search trajectory networks (STNs) to analyse, visualise and directly contrast the behaviour of different neuroevolution search methods. Our analysis uses NEAT for solving maze problems with two search strategies: novelty-based and fitness-based, and including and excluding the crossover operator. We model and visualise the trajectories, contrasting and illuminating the behaviour of the studied neuroevolution variants. Our results confirm the advantages of novelty search in this setting, but challenge the usefulness of recombination.

Keywords
Neuroevolution; NEAT; Algorithm analysis; Complex networks; Search trajectory networks; Novelty search; Recombination

Journal
SN Computer Science: Volume 3, Issue 3

People

Dr Jason Adair

Lecturer in Data Science, Computing Science

Professor Gabriela Ochoa

Professor, Computing Science

Mr Stefano Sarti

Tutor, Computing Science and Mathematics - Division