Genetic Programming Theory and Practice V was developed from the fifth workshop at the University of Michigan’s Center for the Study of Complex Systems to facilitate the exchange of ideas and information related to the rapidly advancing field of Genetic Programming (GP). Contributions from the foremost international researchers and practitioners in the GP arena examine the similarities and differences between theoretical and empirical results on real-world problems. The text explores the synergy between theory and practice, producing a comprehensive view of the state of the art in GP application. The work covers applications of GP to a wide variety of domains, including bioinformatics, symbolic regression for system modeling, financial modeling, circuit design and robot controllers. This volume is a unique and indispensable tool for academics, researchers and industry professionals involved in GP, evolutionary computation, machine learning and artificial intelligence. Discusses hurdles in solving large-scale applications. Describes techniques including fitness and age layered populations, code reuse through caching, archives and libraries, Pareto optimization, pre- and post-processing, the use of expert knowledge and information-theoretic fitness measures. Addresses practical methods for choosing betweentechniques for improving GP performance and to evolve trustable solutions. INDICE: Contributing Authors.- Preface.- Foreword.- Genetic Programming: Theory and Practice.- Soft Evolution of Robust Regression Models.- Manipulationof Converence in Evolutionary Systems.- Large-Scale, Time-Constrained Symbolic Regression-Classification.- Solving Complex Problems in Human Genetics UsingGenetic Programming.- Information Theoretic Framework.- Investigating ProblemHardness in Real Life Applications.- Improving Scalability of Generative Representations.- Program Structure-Fitness Disconnect and Its Impact on Evolutionin GP.- Genetic Programming with Reuse of Known Designs.- Robust Design of Electronic Circuits.- Trustable Symbolic Regression Models.- Improving Performance and Cooperation.- An Empirical Study of Multi-Objective Algorithms for Stock Ranking.- Simulating the Evolution of an Ancient Urban Center.- Index.
- ISBN: 978-0-387-76307-1
- Editorial: Springer
- Encuadernacion: Cartoné
- Páginas: 300
- Fecha Publicación: 01/02/2008
- Nº Volúmenes: 1
- Idioma: Inglés