- 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.",
