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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 between techniques for improving GP performance and to evolve trustable solutions
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.
Specific topics addressed in the book include:
the hurdles faced in solving large-scale, cutting edge applications
promising techniques, including fitness and age layered populations, code reuse through caching, archives and run transferable libraries, Pareto optimization, and pre- and post-processing
the use of information theoretic measures and ensemble techniques
approaches to help GP create trustable solutions
the use of expert knowledge to guide GP
ways to make GP tools more accessible to the non-GP-expert
practical methods for understanding and choosing between the recent proliferation of techniques for improving GP performance
the potential for GP to undergo radical changes to accommodate the expanded understanding of biological genetics and evolution
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.
Genetic Programming: Theory and Practice.- Better Solutions Faster: Soft Evolution of Robust Regression Models InParetogeneticprogramming.- Manipulation of Convergence in Evolutionary Systems.- Large-Scale, Time-Constrained Symbolic Regression-Classification.- Solving Complex Problems in Human Genetics Using Genetic Programming: The Importance of Theorist-Practitionercomputer Interaction.- Towards an Information Theoretic Framework for Genetic Programming.- Investigating Problem Hardness of Real Life Applications.- Improving the Scalability of Generative Representations for Openended Design.- Programstructure-Fitnessdisconnect and Its Impact on Evolution in Genetic Programming.- Genetic Programmingwith Reuse of Known Designs for Industrially Scalable, Novel Circuit Design.- Robust engineering design of electronic circuits with active components using genetic programming and bond Graphs.- Trustable symbolic regression models: using ensembles, interval arithmetic and pareto fronts to develop robust and trust-aware models.- Improving Performance and Cooperation in Multi-Agent Systems.- An Empirical Study of Multi-Objective Algorithms for Stock Ranking.- Using GP and Cultural Algorithms to Simulate the Evolution of an Ancient Urban Center.