Genetic Programming Theory and Practice V

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.