- ホーム
- > 洋書
- > 英文書
- > Computer / General
Full Description
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 volume 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.
Contents
Chapter 1. On the Effects of Continuous Pruning on Symbolic Regression for Different Variants of Evolutionary Search.- Chapter 2. Analyzing Fitness Aggregation Strategies for Symbolic Regression Problem-Solving.- Chapter 3. The Evolution of Heterogeneous Logic: An Analysis of the Buffet Method.- Chapter 4. FPGA-Based Streaming Processors for Tree-Based Genetic Programming.- Chapter 5. On Interpretability in Multimodal Biomedical Image Analysis.- Chapter 6. CANTS-GP: A Nature-Inspired Metaheuristic for Graph Based Genetic Programs.- Chapter 7. Bridging Genetic Programming and Type Theory Research.- Chapter 8. To Smoothly Go Where No Model has Gone Before: Pareto Tournaments, Model Curvature and Alternating Objectives.- Chapter 9. GP and LLMs for Program Synthesis: No ClearWinners.- Chapter 10. Offline reinforcement learning: A New Challenge for Symbolic Regression.- Chapter 11. Language Model-Driven Program Synthesis with Program Trace Optimization on the Abstraction and Reasoning Corpus.- Chapter 12. Evolving Programs in the Lambda Calculus using Program Trace Optimisation.- Chapter 13. Interpretable Control with Graph-based Genetic Programming.- Chapter 14. Decoupling Representation and Learning in Genetic Programming:the LaSER Approach.- Chapter 15. Tips on Effective Theory and Practice of Genetic Programming.- Chapter 16. Spatial Genetic Programming with the S1 Processing Board.- Chapter 17. Applications of Evolutionary Algorithms for Instrument Design.- Chapter 18. Agentic GP: A Theoretical Framework for the Development of Genetic Programming Systems via Agentic AI.- Chapter 19. Evolution of Artificial Intelligence, Continued.- Chapter 20. Heeding Good Advice: Scaling Down and Specializing in the Age of Big AI.- Chapter 21. The Gegelati Framework for Efficient and Reproducible Solutions with Tangled Program Graphs.
