Genetic Programming Theory and Practice VI was developed from the sixth 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.
These contributions address several significant interdependent themes which emerged from this year’s workshop, including: (1) Making efficient and effective use of test data. (2) Sustaining the long-term evolvability of our GP systems. (3) Exploiting discovered subsolutions for reuse. (4) Increasing the role of a Domain Expert.
Inhoudsopgave
Genetic Programming: Theory and Practice.- APopulation Based Study of Evolutionary Dynamics in Genetic Programming.- An Application of Information Theoretic Selection to Evolution of Models with Continuous-valued Inputs.- Pareto Cooperative-Competitive Genetic Programming: A Classification Benchmarking Study.- Genetic Programming with Historically Assessed Hardness.- Crossover and Sampling Biases on Nearly Uniform Landscapes.- Analysis of the Effects of Elitismon Bloat in Linear and Tree-based Genetic Programming.- Automated Extraction of Expert Domain Knowledge from Genetic Programming Synthesis Results.- Does Complexity Matter? Artificial Evolution, Computational Evolution and the Genetic Analysis of Epistasis in Common Human Diseases..- Exploiting Trustable Models via Pareto GP for Targeted Data Collection.- Evolving Effective Incremental Solvers for SAT with a Hyper-Heuristic Framework Based on Genetic Programming.- Constrained Genetic Programming to Minimize Overfitting in Stock Selection.- Co-Evolving Trading Strategies to Analyze Bounded Rationality in Double Auction Markets..- Profiling Symbolic Regression-Classification.- Accelerating Genetic Programming through Graphics Processing Units..- Genetic Programming for Incentive-Based Design within a Cultural Algorithms Framework..
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