Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@InProceedings{Miller:2015:GECCOcomp, author = "Julian Miller and Andrew Turner", title = "Cartesian Genetic Programming", booktitle = "GECCO 2015 Introductory Tutorials", year = "2015", editor = "Anabela Simoes", isbn13 = "978-1-4503-3488-4", keywords = "genetic algorithms, genetic programming, cartesian genetic programming", pages = "179--198", month = "11-15 " # jul, organisation = "SIGEVO", address = "Madrid, Spain", URL = "http://doi.acm.org/10.1145/2739482.2756571", DOI = "doi:10.1145/2739482.2756571", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "Cartesian Genetic Programming (CGP) is a well-known form of Genetic Programming developed by Julian Miller in 1999-2000. In its classic form, it uses a very simple integer address-based genetic representation of a program in the form of a directed graph. Graphs are very useful program representations and can be applied to many domains (e.g. electronic circuits, neural networks). It can handle cyclic or acyclic graphs. In a number of studies, CGP has been shown to be comparatively efficient to other GP techniques. It is also very simple to program. The classical form of CGP has undergone a number of developments which have made it more useful, efficient and flexible in various ways. These include self-modifying CGP (SMCGP), cyclic connections (recurrent-CGP), encoding artificial neural networks and automatically defined functions (modular CGP). SMCGP uses functions that cause the evolved programs to change themselves as a function of time. This makes it possible to find general solutions to classes of problems and mathematical algorithms (e.g. arbitrary parity, n-bit binary addition, sequences that provably compute pi and e to arbitrary precision, and so on). Recurrent-CGP allows evolution to create programs which contain cyclic, as well as acyclic, connections. This enables application to tasks which require internal states or memory. It also allows CGP to create recursive equations. CGP encoded artificial neural networks represent a powerful training method for neural networks. This is because CGP is able to simultaneously evolve the networks connections weights, topology and neuron transfer functions. It is also compatible with Recurrent-CGP enabling the evolution of recurrent neural networks. The tutorial will cover the basic technique, advanced developments and applications to a variety of problem domains. It will present a live demo of how the open source cgp library can be used.", notes = "Also known as \cite{2756571} Distributed at GECCO-2015.", }

Genetic Programming entries for Julian F Miller Andrew James Turner