IEEE Congress on Evolutionary Computation
Trondheim, Norway, 18th-21st May, 2009 Special Session Organisers
James Alfred Walker jaw500@ohm.york.ac.uk Julian Francis Miller jfm7@ohm.york.ac.uk Intelligent Systems Group, Department of Electronics, University of York, UK.
Special Session on Cartesian Genetic Programming (CGP)
Cartesian Genetic Programming is a form of genetic programming developed by Julian Miller and Peter Thomson in 1997. In its classic form, CGP represents a program as a directed graph using a very simple integer-based representation. In a number of studies, it has been shown to be comparatively efficient when compared to other GP techniques. Since then, the classical form of CGP has been enhanced in various ways to include automatically defined functions (ADFs), multiple chromosomes, and most recently, self-modification and crossover operators. In addition, CGP has also been applied to a number of novel and real-world applications in academia and industry. This is the first special session given on this increasingly popular form of GP. The aim of this special session is to reflect recent advances and state-of-theart developments in CGP, to allow the opportunity for CGP practitioners to discuss key issues and exchange new ideas regarding CGP, to raise awareness and the profile of CGP to a wider audience, and to encourage the growth of the CGP community. The submission of technical and position papers is invited on all aspects of CGP. Topics of interest include, but are not limited to: ● ● ● ● ● ● ● ● ● ● ● ● Applications of CGP to novel and real-world problems Applications Alternative representations and operators for CGP Alternative op Exploiting modularity and problem decomposition in CGP Exploiting Novel CGP frameworks Novel Algorithms using CGP for development and development-based CGP Algorithms algorithms algorithms Analytical, empirical or theoretical analysis that enhances the Analytical understanding of CGP understanding Hybridisation of CGP with other evolutionary and bio-inspired Hybrid algorithms algorithms CGP performance benchmarks and comparisons CGP perf Evolutionary art, sculpture, and music using CGP Evolutionary Scalability of CGP Scalability CGP for learning and game-playing CGP learning CGP algorithms and applications using GPUs CGP algorithms

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