In this paper, we review the key features and major drawbacks of the Neuroevolution of Augmenting Topologies (NEAT) algorithm, such as slow training speed that limits its area of application. The main reason for the performance issues of the NEAT algorithm is the huge number of calculations required at the end of each epoch to estimate the fitness of each organism in the population. We propose a software system architecture that can be implemented to solve NEAT performance problems based on Ray cluster-computing framework. Finally, we demonstrate how fitness estimation computations can be distributed across stateless distributed workers deployed either on-premise or in the cloud using Ray framework.

Machine Learning and the City: Applications in Architecture and Urban Design delivers a robust exploration of machine learning (ML) and artificial intelligence (AI) in the context of the built environment. Relevant contributions from leading scholars in their respective fields describe the ideas and techniques that underpin ML and AI, how to begin using ML and AI in urban design, and the likely impact of ML and AI on the future of city design and planning.

Neuroevolution is a form of artificial intelligence learning that uses evolutionary algorithms to simplify the process of solving complex tasks in domains such as games, robotics, and the simulation of natural processes. This book will give you comprehensive insights into essential neuroevolution concepts and equip you with the skills you need to apply neuroevolution-based algorithms to solve practical, real-world problems.

In this paper, we look at how Artificial Swarm Intelligence can evolve using evolutionary algorithms that try to minimize the sensory surprise of the system. We will show how to apply the free-energy principle, borrowed from statistical physics, to quantitatively describe the optimization method (sensory surprise minimization), which can be used to support lifelong learning.