Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
Proceedings of the 3rd International Conference on Genetic Algorithms
A Memetic Pareto Evolutionary Approach to Artificial Neural Networks
AI '01 Proceedings of the 14th Australian Joint Conference on Artificial Intelligence: Advances in Artificial Intelligence
Advances in evolutionary computing
Evolutionary product unit based neural networks for regression
Neural Networks
Computers and Electronics in Agriculture
Using genetic programming for artificial neural network development and simplification
CIMMACS'06 Proceedings of the 5th WSEAS International Conference on Computational Intelligence, Man-Machine Systems and Cybernetics
Automatic Design of ANNs by Means of GP for Data Mining Tasks: Iris Flower Classification Problem
ICANNGA '07 Proceedings of the 8th international conference on Adaptive and Natural Computing Algorithms, Part I
A Novel Global Hybrid Algorithm for Feedforward Neural Networks
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks, Part III
Computers and Electronics in Agriculture
Evolutionary product-unit neural networks classifiers
Neurocomputing
Automatic Recurrent ANN development for signal classification: detection of seizures in EEGs
Proceedings of the 2008 conference on ECAI 2008: 18th European Conference on Artificial Intelligence
Evolving simple feed-forward and recurrent ANNs for signal classification: a comparison
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
IWANN'07 Proceedings of the 9th international work conference on Artificial neural networks
Hybrid evolutionary algorithm with product-unit neural networks for classification
IWANN'07 Proceedings of the 9th international work conference on Artificial neural networks
A global optimization algorithm based on novel interval analysis for training neural networks
ISICA'07 Proceedings of the 2nd international conference on Advances in computation and intelligence
A two-stage algorithm in evolutionary product unit neural networks for classification
Expert Systems with Applications: An International Journal
An evolutionary artificial neural networks approach for breast cancer diagnosis
Artificial Intelligence in Medicine
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Product unit neural networks are useful because they can handle higher order combinations of inputs. When trained using traditional backpropagation, however, they are often susceptible to local minima. The use of genetic algorithm exploratory procedures that can often locate near-optimal solutions to complex problems to overcome this, is discussed. The genetic algorithm maintains a set of trial solutions and forces them to evolve toward an acceptable solution. A representation for possible solutions must first be developed. Then, with an initial random population, the algorithm uses survival of the fittest techniques as well as old knowledge in the gene pool to improve each generation's ability to solve the problem. This improvement is achieved through a four-step process of evaluation, reproduction, breeding, and mutation. An example application is described.