This book synthesizes the state of the art in the science and technology of robot evolution. It explores the principle “If evolution can create intelligence, artificial evolution can create artificial intelligence”. It centers on embodied AI, where intelligence emerges from the interaction between a physical body and its environment, as exemplified by robots. The long-term vision is a new class of intelligent machines that evolve, learn, and improve both their bodies and their brains ‘on the job’.
Designed as both an accessible entry point for students and a comprehensive reference for experts, the book is rich in case studies, design patterns, and adaptable algorithmic recipes that readers can reuse and extend. Written as a didactic guide, it supports both self-study and teaching. To bridge theory and practice, the book is accompanied by open-source software for experimentation, prototyping, and reproducible research.
Students in artificial intelligence, computer science, robotics, and related fields will find this volume a clear and structured introduction, while researchers, AI experts, roboticists, and biologists can use it as a valuable reference work and an inspiration for their research.
About the Author :
Agoston E. Eiben is Professor of Computational Intelligence at Vrije Universiteit Amsterdam. He is a world-leading expert in evolutionary computing and evolutionary robotics, and (co-)author of the best-selling Introduction to Evolutionary Computing. His current research explores robots that can reproduce, evolve, and learn. He has published in leading venues such as Nature, Science Robotics, and Nature Machine Intelligence, and his work has received extensive international media coverage in more than ten countries. His long-term goal is to show how artificial evolution can yield physical artificial intelligence, and to deepen our understanding of the interplay between the body and the brain.
Karine Miras is an Assistant Professor of Computational Intelligence at Vrije Universiteit Amsterdam, working at the intersection of Evolutionary Robotics and Artificial Life. Her research has the goal of advancing autonomous systems and also deepening our understanding of biological evolution. At the core of her work is exploring how environments shape the traits of evolving creatures, and how developmental processes can be embedded directly into the genetic architectures of artificial life forms. "I'm interested in understanding what allows for the evolution of complex life through the study of artificial life".
Emma Hart is a Professor of Computational Intelligence at Edinburgh Napier University, Scotland, UK. Her research interests lie in the cross-cutting areas of evolutionary robotics, artificial intelligence, and optimisation, with a particular focus on combining methods from evolutionary computing and machine-learning to develop systems that learn autonomously over time and improve their own performance with experience, whether in robotics or in solving real-world optimisation problems. She is Fellow of the Royal Society of Edinburgh, and a Senior Member of ACM.
