Genetic Programming idea and perform VI was once constructed from the 6th workshop on the collage of Michigan's heart for the examine of advanced platforms to facilitate the alternate of principles and knowledge regarding the speedily advancing box of Genetic Programming (GP).
Contributions from the major overseas researchers and practitioners within the GP enviornment learn the similarities and alterations among theoretical and empirical effects on real-world difficulties. The textual content explores the synergy among idea and perform, generating a complete view of the state-of-the-art in GP application.
These contributions handle a number of major inter-dependent subject matters which emerged from this year's workshop, including:
* Making effective and potent use of attempt data
* maintaining the longer term evolvability of our GP systems
* Exploiting came across subsolutions for reuse
* expanding the function of a site Expert
In the process investigating those subject matters, the chapters describe numerous options in frequent use between practitioners who care for industrial-scale, real-world difficulties, corresponding to:
* Pareto optimization, rather as a method to restrict answer complexity
* a number of varieties of age-layered populations or niching mechanisms
* info partitioning, a priori or adaptively, e.g., through co-evolution
* Cluster computing or common goal photos processors for parallel computing
* Ensemble/team solutions
This paintings covers purposes of GP to a bunch of domain names, together with bioinformatics, symbolic regression for method modeling in quite a few settings, circuit layout, and monetary modeling to help portfolio management.
This quantity is a special and imperative software for lecturers, researchers and execs serious about GP, evolutionary computation, computer studying and synthetic intelligence.
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Extra resources for Genetic Programming Theory and Practice VI (Genetic and Evolutionary Computation)
Springer, Ann Arbor. , Radman, Miroslav, and Ramsden, Jeremy J. (2006). From artificial evolution to computational evolution: a research agenda. In Nature Reviews: Genetics, volume 7, page 729—735. , and Francone, Frank D. (1998). Genetic Programming – An Introduction; On the Automatic Evolution of Computer Programs and its Applications. Morgan Kaufmann, San Francisco, CA, USA. Becker, Ying, Fei, Peng, and Lester, Anna M. (2006). Stock selection : An innovative application of genetic programming methodology.
McConaghy, Trent, Palmers, Pieter, Gielen, Georges, and Steyaert, Michiel (2007). Genetic programming with reuse of known designs. , Soule, Terence, and Worzel, Bill, editors, Genetic Programming Theory and Practice V, Genetic and Evolutionary Computation, chapter 10, pages 161– 186. Springer, Ann Arbor. , Barney, Nate, and White, Bill C. (2007). Solving complex problems in human genetics using genetic programming. , Soule, Terence, and Worzel, Bill, editors, Genetic Programming Theory and Practice V, Genetic and Evolutionary Computation, chapter 5, pages 69–86.
Acknowledgements The authors thank the participants in the Genetic Programming in Theory and Practice 2007 workshop for the discussions that led to this work, especially Martin Keane for asking pointed questions and Michael Korns for supplying the test function data set. References Abayomi, Kobi Ako, Lall, Upmanu, and de la Pena, Victor H. (2007). Copula based independent component analysis. Working paper, Duke University and Unknown and Columbia University. com/abstract=1028822. Card, Stuart W.