Dennis Wilson has released his implementation of CGP in Julia.


Dario Izzo, Francesco Biscani, and Alessio Mereta of the Advanced Concepts Team, European Space Agency in Netherlands have created a differentiable form of CGP(dCGP). This allows one to obtain high-order Taylor representation of program outputs. The new technique means that back-propagation (as in neural networks) can be used.


is open source and is able to find the exact form of symbolic expression as well as their constants. The authors demonstrate the use of dCGP to solve a large class of differential equations and to find prime integrals of dynamical systems.

GOLD MEDAL: Zdenek Vasicek and Lukas Sekanina won gold humies ($5000) award at GECCO 2015

Congratulations to Zdenek Vasicek and Lukas Sekanina for their oustanding work on "Evolutionary Approach to Approximate Digital Circuits Design". They used CGP :-)

Zdenek Vasicek wins best paper at EuroGP2015

Congratulations to Zdenek Vasicek who won best paper award at EuroGP2015 for his paper "Cartesian GP in Optimization of Combinational Circuits with Hundreds of Inputs and Thousands of Gates".

At the Evostar 2015 Conference there were a number of papers which used CGP.

Cartesian GP in Optimization of Combinational Circuits with Hundreds of Inputs and Thousands of Gates
Zdenek Vasicek

Evolutionary Design of Transistor Level Digital Circuits Using Discrete Simulation
Vojtech Mrazek and Zdenek Vasicek

Indirectly Encoded Fitness Predictors Coevolved with Cartesian Programs
Michaela Sikulova, Jiri Hulva, and Lukas Sekanina

Evolutionary Methods for the Construction of Cryptographic Boolean Functions
Stjepan Picek, Domagoj Jakobovic1, Julian F. Miller, Elena Marchiori and Lejla Batina

Circuit Approximation Using Single- and Multi-objective Cartesian GP
Zdenek Vasicek and Lukas Sekanina

A Fast FPGA-Based Classification of Application Protocols Optimized Using Cartesian GP
David Grochol, Lukas Sekanina, Martin Zadnik, and Jan Korenek

A number of events related to CGP happened in 2014 at the Parallel Problem Solving from Nature conference.

PPSN 2014 (Ljubljana, Slovenia)

Julian Miller is giving a tutorial on Cartesian Genetic Programming.

Andy Turner and Julian Miller had a paper on Recurrent Cartesian Genetic Programming.

There are two other papers on evolving solutions to computational problems using materials (e.g. carbon nanotubes) where CGP has been used for comparative purposes

Evolution-In-Materio: Solving Machine Learning Classification Problems Using Materials.
Maktuba Mohid, Julian F. Miller, Simon L. Harding, Gunnar Tufte, Odd Rune Lykkebo, Kieran Massey, and Mike Petty.

Travelling Salesman Problem solved 'in materio' by evolved carbon nanotube device.
Kester Dean Clegg, Julian Francis Miller, Kieran Massey, and Mike Petty.

Bent Function Synthesis by Means of Cartesian Genetic Programming.
Radek Hrbacek and Vaclav Dvorak (this work resulted in a human-competitive award at GECCO 2015).

Evostar 2014 (Granada,Spain)

There were 2 papers relating to CGP at the Evostar Conference.

At EuroGP

Cartesian Genetic Programming: Why No Bloat? Andrew Turner, Julian F. Miller

On Evolution of Multi-Category Pattern Classifiers Suitable for Embedded Systems. Zdenek Vasicek, Michal Bidlo

AISB50 (Goldsmith's College,London, UK)

At the 50th anniversary AISB conference there was a paper on CGP artificial neural networks

At Evo20

The Importance of Transfer Function Evolution and Heterogeneous Networks Andrew Turner, Julian F. Miller

AI2013 (Peterhouse College,Cambridge, UK)

In December 2013 the 34th SGAI International Conference on Artificial Intelligence took place in Cambridge. There was a paper on CGP artificial neural networks

The Importance of Topology Evolution in NeuroEvolution: A Case Study using Cartesian Genetic Programming of Artificial Neural Networks
Andrew Turner, Julian F. Miller

CGP Image Filters
CGP is being used in a European robotics project. Simon Harding has developed a neat way of using CGP to evolve object recognition image filters. Once the filters are evolved, they can operate extremely fast and can pick out objects of interest in images. The filters are orientation independent and still work very well under different lighting conditions. There is a nice video at IM-CLeVeR European project Here is an video of an evolved object recognition program. It detects a packet of tea!. It can detect the packet in any orientation, proximity and lighting condition.

Google Lunar X prize
CGP is being used by a team who are aiming to win the Google Lunar X prize. This is a prize awarded to the first private venture to land a rover vehicle on the moon. Wes Faler, who is a member of a team called "Part-time scientists" are building a rover called "Asimov 1". Wes gave talks at a conference called 28C3 in Berlin, Germany about two ways he is using CGP in the project. His talks are available on his blog

Look at the "People" tab for places to download these papers.

If you have news about CGP and want it linked here, please inform Julian Miller.