Genetic Programming Theory and Practice 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.

Explores the intersection of GP and evolutionary computation, with machine learning and deep learning methods Provides a unique combination of theoretical contributions and state-of-the-art real-world problem solving with GP Discusses novel selection strategies, modular architectures, and unique fitness assignment strategies

Inhalt

Chapter 1. Symbolic Regression and Real World Applications.- Chapter 2. Program Synthesis with GP plus others.- Chapter 3. Machine learning and GP.- Chapter 4. Grammatical Evolution and Medical Applications of GP.- Chapter 5. Evolved Analytics LLC, Efficient Real-World Problem Solving with Genetic Programming.- Chapter 6. Automatic Machine Learning with GP.- Chapter 7. GP and Cybersecurity.- Transfer Learning and GP.- Chapter 8. Selection Mechanisms in Genetic Programming.- Chapter 9. Evolutionary Computation and Machine Learning.