Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Efficient reinforcement learning through symbiotic evolution
Machine Learning - Special issue on reinforcement learning
Evolving the Topology and the Weights of Neural Networks Using a Dual Representation
Applied Intelligence
The Advantages of Landscape Neutrality in Digital Circuit Evolution
ICES '00 Proceedings of the Third International Conference on Evolvable Systems: From Biology to Hardware
Proceedings of the European Conference on Genetic Programming
Neutrality and the Evolvability of Boolean Function Landscape
EuroGP '01 Proceedings of the 4th European Conference on Genetic Programming
Supervised fuzzy clustering for the identification of fuzzy classifiers
Pattern Recognition Letters
Fast Reinforcement Learning through Eugenic Neuro-Evolution
Fast Reinforcement Learning through Eugenic Neuro-Evolution
Robust non-linear control through neuroevolution
Robust non-linear control through neuroevolution
Efficient evolution of neural networks through complexification
Efficient evolution of neural networks through complexification
Efficient evolution of neural network topologies
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
Breast cancer diagnosis using least square support vector machine
Digital Signal Processing
A developmental model of neural computation using cartesian genetic programming
Proceedings of the 9th annual conference companion on Genetic and evolutionary computation
Developing neural structure of two agents that play checkers using cartesian genetic programming
Proceedings of the 10th annual conference companion on Genetic and evolutionary computation
Accelerated Neural Evolution through Cooperatively Coevolved Synapses
The Journal of Machine Learning Research
An expert system for detection of breast cancer based on association rules and neural network
Expert Systems with Applications: An International Journal
A comparison between cellular encoding and direct encoding for genetic neural networks
GECCO '96 Proceedings of the 1st annual conference on Genetic and evolutionary computation
Solving non-Markovian control tasks with neuroevolution
IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2
Harmony Search Based Supervised Training of Artificial Neural Networks
ISMS '10 Proceedings of the 2010 International Conference on Intelligent Systems, Modelling and Simulation
A linear learning method for multilayer perceptrons using least-squares
IDEAL'07 Proceedings of the 8th international conference on Intelligent data engineering and automated learning
Evolving neural networks in compressed weight space
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Neural Network Classifier with Entropy Based Feature Selection on Breast Cancer Diagnosis
Journal of Medical Systems
MIMO CMAC neural network classifier for solving classification problems
Applied Soft Computing
Redundancy and computational efficiency in Cartesian genetic programming
IEEE Transactions on Evolutionary Computation
A new evolutionary system for evolving artificial neural networks
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
EvoBIO'13 Proceedings of the 11th European conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics
Bio-signal Processing Using Cartesian Genetic Programming Evolved Artificial Neural Network (CGPANN)
FIT '12 Proceedings of the 2012 10th International Conference on Frontiers of Information Technology
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Neuroevolution, the application of evolutionary algorithms to artificial neural networks (ANNs), is well-established in machine learning. Cartesian Genetic Programming (CGP) is a graph-based form of Genetic Programming which can easily represent ANNs. Cartesian Genetic Programming encoded ANNs (CGPANNs) can evolve every aspect of an ANN: weights, topology, arity and node transfer functions. This makes CGPANNs very suited to situations where appropriate configurations are not known in advance. The effectiveness of CGPANNs is compared with a large number of previous methods on three benchmark problems. The results show that CGPANNs perform as well as or better than many other approaches. We also discuss the strength and weaknesses of each of the three benchmarks.