Since the early 1990s, genetic programming (GP) a discipline whose goal is to enable the automatic generation of computer programs has emerged as one of the most promising paradigms for fast, productive software development. GP combines biological metaphors gleaned from Darwin's theory of evolution with computer-science approaches drawn from the field of machine learning to create programs that are capable of adapting or recreating themselves for open-ended tasks.
This unique introduction to GP provides a detailed overview of the subject and its antecedents, with extensive references to the published and online literature. In addition to explaining the fundamental theory and important algorithms, the text includes practical discussions covering a wealth of potential applications and real-world implementation techniques. Software professionals needing to understand and apply GP concepts will find this book an invaluable practical and theoretical guide."

Linear Genetic Programming presents a variant of genetic programming (GP) that evolves imperative computer programs as linear sequences of instructions, in contrast to the more traditional functional expressions or syntax trees. Primary characteristics of linear program structure are exploited to achieve acceleration of both execution time and evolutionary progress. Online analysis and optimization of program code lead to more efficient techniques and contribute to a better understanding of the method and its parameters. In particular, the reduction of structural variation step size and non-effective variations play a key role in finding higher quality and less complex solutions. Typical GP phenomena, such as non-effective code, neutral variations, and code growth are investigated from the perspective of linear GP. This book serves as a reference for researchers; it also contains sufficient introductory material for students and those who are new to the field.

These contributions, written by the foremost international researchers and practitioners of Genetic Programming (GP), explore the synergy between theoretical and empirical results on real-world problems, producing a comprehensive view of the state of the art in GP. Topics in this volume include: exploiting subprograms in genetic programming, schema frequencies in GP, Accessible AI, GP for Big Data, lexicase selection, symbolic regression techniques, co-evolution of GP and LCS, and applying ecological principles to GP. It also covers several chapters on best practices and lessons learned from hands-on experience. Readers will discover large-scale, real-world applications of GP to a variety of problem domains via in-depth presentations of the latest and most significant results.

This book brings together some of the most impactful researchers in the field of Genetic Programming (GP), each one working on unique and interesting intersections of theoretical development and practical applications of this evolutionary-based machine learning paradigm. Topics of particular interest for this year´s book include powerful modeling techniques through GP-based symbolic regression, novel selection mechanisms that help guide the evolutionary process, modular approaches to GP, and applications in cybersecurity, biomedicine and program synthesis, as well as papers by practitioner of GP that focus on usability and real-world results. In summary, readers will get a glimpse of the current state of the art in GP research.

These contributions, written by the foremost international researchers and practitioners of Genetic Programming (GP), explore the synergy between theoretical and empirical results on real-world problems, producing a comprehensive view of the state of the art in GP. In this year's edition, the topics covered include many of the most important issues and research questions in the field, such as: opportune application domains for GP-based methods, game playing and co-evolutionary search, symbolic regression and efficient learning strategies, encodings and representations for GP, schema theorems, and new selection mechanisms.The volume includes several chapters on best practices and lessons learned from hands-on experience. Readers will discover large-scale, real-world applications of GP to a variety of problem domains via in-depth presentations of the latest and most significant results.

This book, written by leading international researchers of evolutionary approaches to machine learning, explores various ways evolution can address machine learning problems and improve current methods of machine learning. Topics in this book are organized into five parts. The first part introduces some fundamental concepts and overviews of evolutionary approaches to the three different classes of learning employed in machine learning. The second addresses the use of evolutionary computation as a machine learning technique describing methodologic improvements for evolutionary clustering, classification, regression, and ensemble learning. The third part explores the connection between evolution and neural networks, in particular the connection to deep learning, generative and adversarial models as well as the exciting potential of evolution with large language models. The fourth part focuses on the use of evolutionary computation for supporting machine learning methods. This includes methodological developments for evolutionary data preparation, model parametrization, design, and validation. The final part covers several chapters on applications in medicine, robotics, science, finance, and other disciplines. Readers find reviews of application areas and can discover large-scale, real-world applications of evolutionary machine learning to a variety of problem domains. This book will serve as an essential reference for researchers, postgraduate students, practitioners in industry and all those interested in evolutionary approaches to machine learning.

This edited research monograph brings together contributions from computer scientists, biologists, and engineers who are engaged with the study of evolution and how it may be applied to solve real-world problems. It also serves as a Festschrift dedicated to Erik D. Goodman, the founding director of the BEACON Center for the Study of Evolution in Action, a pioneering NSF Science and Technology Center headquartered at Michigan State University. The contributing authors are leading experts associated with the center, and they serve in top research and industrial establishments across the US and worldwide. Part I summarizes the history of the BEACON Center, with refreshingly personal chapters that describe Erik's working and leadership style, and others that discuss the development and successes of the center in the context of research funding, projects, and careers. The chapters in Part II deal with the evolution of genomes and evolvability. The contributions in Part III discuss the evolution of behavior and intelligence. Those in Part IV concentrate on the evolution of communities and collective dynamics. The chapters in Part V discuss selected evolutionary computing applications in domains such as arts and science, automated program repair, cybersecurity, mechatronics, and genomic prediction. Part VI deals with evolution in the classroom, using creativity in research, and responsible conduct in research training. The book concludes with a special chapter from Erik Goodman, a short biography that concentrates on his personal positive influences and experiences throughout his long career in academia and industry.

This book, written by the foremost international researchers and practitioners of genetic programming (GP), explores the synergy between theoretical and empirical results on real-world problems, producing a comprehensive view of the state of the art in GP. In this year's edition, the topics covered include many of the most important issues and research questions in the field, such as opportune application domains for GP-based methods, game playing and co-evolutionary search, symbolic regression and efficient learning strategies, encodings and representations for GP, schema theorems, and new selection mechanisms. The book includes several chapters on best practices and lessons learned from hands-on experience. Readers will discover large-scale, real-world applications of GP to a variety of problem domains via in-depth presentations of the latest and most significant results.