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This book constitutes the refereed proceedings of the 27th European Conference on Genetic Programming, EuroGP 2024, held in Aberystwyth, UK, April 3–5, 2024 and co-located with the EvoStar events, EvoCOP, EvoMUSART, and EvoApplications.The 13 papers (9 selected for long presentation and 4 for short presentation) collected in this book were carefully reviewed and selected from 24 submissions. The wide range of topics in this volume reflects the current state of research in the field. The collection of papers cover topics including developing new variants of GP algorithms, as well as exploring GP applications to the optimization of machine learning methods and the evolution of control policies.

This book marks one of the first applications of the Medicine Network discipline to an everyday scenario. It explores situations where patients, often in adolescence, grapple with the decision of whether to commence a treatment, seeking insights into the more plausible future scenarios. Additionally, the specific feedback from biological systems in the human body serves as a potent metaphor for addressing various challenges in the field of Complex Systems. In recent times, systems thinking and complexity theory have yielded substantial conceptual advancements across various research domains. In the context of orthodontics, these approaches offer a more comprehensive understanding in contrast to the traditional mechanistic approach, which primarily focuses on the analysis of applied forces. Systems thinking directs attention to the interaction among dentoskeletal components, where the behavior of one element can influence others. The amalgamation of multiple elements leads to entities with properties distinct from those of individual components. The increasing complexity of orthodontic reality beyond clinical or radiological observations necessitates the development of new theories. Complexity theory has demonstrated that emergent properties in biological systems can be discerned through appropriate computational models, as opposed to the analytical study of individual components. The central metaphor for the interactive craniofacial system during growth is portrayed by the facial topology revealed through network analysis, facilitating a systemic reevaluation of traditional orthodontic theories. This book delineates the novel insights derived from the clinical-computational approach, applicable for a prognostic and early interception perspective in managing dentofacial dysmorphoses. Its objective is to captivate practitioners and persuade them of the practical utility of these innovative approaches.

Focusing on the fundamentals of machine learning, this book covers broad areas of data-driven modeling, ranging from simple regression to advanced machine learning and optimization methods for applications in materials modeling and discovery. The book explains complex mathematical concepts in a lucid manner to ensure that readers from different materials domains are able to use these techniques successfully. A unique feature of this book is its hands-on aspect—each method presented herein is accompanied by a code that implements the method in open-source platforms such as Python. This book is thus aimed at graduate students, researchers, and engineers to enable the use of data-driven methods for understanding and accelerating the discovery of novel materials.