Principles in the Evolutionary Design of Digital Circuits—Part I
Genetic Programming and Evolvable Machines
Performance-Enhanced Genetic Programming
EP '97 Proceedings of the 6th International Conference on Evolutionary Programming VI
The Push3 execution stack and the evolution of control
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Self-generating prototypes for pattern classification
Pattern Recognition
Balancing accuracy and parsimony in genetic programming
Evolutionary Computation
Strongly typed genetic programming
Evolutionary Computation
Genetic programming with polymorphic types and higher-order functions
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Incremental local linear fuzzy classifier in fisher space
EURASIP Journal on Advances in Signal Processing
A survey on the application of genetic programming to classification
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Developments in Cartesian Genetic Programming: self-modifying CGP
Genetic Programming and Evolvable Machines
Intelligent decision support system for breast cancer
ICSI'10 Proceedings of the First international conference on Advances in Swarm Intelligence - Volume Part II
A comparison of linear genetic programming and neural networks inmedical data mining
IEEE Transactions on Evolutionary Computation
Redundancy and computational efficiency in Cartesian genetic programming
IEEE Transactions on Evolutionary Computation
An evolutionary artificial neural networks approach for breast cancer diagnosis
Artificial Intelligence in Medicine
Reducing wasted evaluations in cartesian genetic programming
EuroGP'13 Proceedings of the 16th European conference on Genetic Programming
GECCO 2013 tutorial: cartesian genetic programming
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
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The majority of genetic programming implementations build expressions that only use a single data type. This is in contrast to human engineered programs that typically make use of multiple data types, as this provides the ability to express solutions in a more natural fashion. In this paper, we present a version of Cartesian Genetic Programming that handles multiple data types. We demonstrate that this allows evolution to quickly find competitive, compact, and human readable solutions on multiple classification tasks.