Iaroslav Omelianenko is a CTO and Research Director at the NewGround LLC. His research interests include human-computer interaction, genetic algorithms, neuroevolution of augmented topologies, reinforcement learning, control & optimization, and Neurobiology.
He leads the Research and Development team, which applies genetic algorithms to create artificial neural networks with a minimal footprint to solve a variety of control & optimization tasks as well as do research in brain-computer interfaces.
He has more than 30 years of experience with software design, implementation, and project management. He actively participates in open source projects. He presented research papers as an author at international conferences.
He is an author of the book Hands-On Neuroevolution with Python now available on Amazon. Also, he co-authored the book Machine Learning and the City: Applications in Architecture and Urban Design.
Master of Engineering, Industrial Process Management, 1999
Ukrainian State University of Food Technologies
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.