Genetic Programming Theory and Practice V

1. Genetic Programming: Theory and Practice
Terence Soule, Rick L. Riolo, Bill Worzel

2. Better Solutions Faster: Soft Evolution of Robust Regression Models InParetogeneticprogramming
Ekaterina Vladislavleva, Guido Smits, Mark Kotanchek

3. Manipulation of Convergence in Evolutionary Systems
Gearoid Murphy, Conor Ryan

4. Large-Scale, Time-Constrained Symbolic Regression-Classification
Michael F. Korns

5. Solving Complex Problems in Human Genetics Using Genetic Programming: The Importance of Theorist-Practitionercomputer Interaction
Jason H. Moore, Nate Barney, Bill C. White

6. Towards an Information Theoretic Framework for Genetic Programming
Stuart W. Card, Chilukuri K. Mohan

7. Investigating Problem Hardness of Real Life Applications
Leonardo Vanneschi

8. Improving the Scalability of Generative Representations for Openended Design
Gregory S. Hornby

9. Programstructure-Fitnessdisconnect and Its Impact on Evolution in Genetic Programming
A. A. Almal, C. D. MacLean, W. P. Worzel

10. Genetic Programmingwith Reuse of Known Designs for Industrially Scalable, Novel Circuit Design
Trent McConaghy, Pieter Palmers, Georges Gielen, Michiel Steyaert

11. Robust engineering design of electronic circuits with active components using genetic programming and bond Graphs
Xiangdong Peng, Erik D. Goodman, Ronald C. Rosenberg

12. Trustable symbolic regression models: using ensembles, interval arithmetic and pareto fronts to develop robust and trust-aware models
Mark Kotanchek, Guido Smits, Ekaterina Vladislavleva

13. Improving Performance and Cooperation in Multi-Agent Systems
Terence Soule, Robert B. Heckendorn

14. An Empirical Study of Multi-Objective Algorithms for Stock Ranking
Ying L. Becker, Harold Fox, Peng Fei

15. Using GP and Cultural Algorithms to Simulate the Evolution of an Ancient Urban Center
Robert G. Reynolds, Mostafa Z. Ali, Patrick Franzel

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