Neurocomputing
Introduction to the theory of neural computation
Introduction to the theory of neural computation
Using genetic search to exploit the emergent behavior of neural networks
Emergent computation
Fundamentals of neural networks: architectures, algorithms, and applications
Fundamentals of neural networks: architectures, algorithms, and applications
Selected papers of the sixth annual Oregon workshop on Software metrics
An introduction to genetic algorithms
An introduction to genetic algorithms
Genetic Algorithms and Robotics
Genetic Algorithms and Robotics
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Using Neural Networks in Reliability Prediction
IEEE Software
Designing Neural Networks using Genetic Algorithms
Proceedings of the 3rd International Conference on Genetic Algorithms
Training feedforward neural networks using genetic algorithms
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 1
Evolutionary Neural Networks: A Robust Approach to Software Reliability Problems
ISSRE '97 Proceedings of the Eighth International Symposium on Software Reliability Engineering
On-line prediction of software reliability using an evolutionary connectionist model
Journal of Systems and Software
Evolutionary software engineering, a review
Applied Soft Computing
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The genetic algorithm is applied to developing optimal or near optimal backpropagation neural networks for fault-prone/not-fault-prone classification of software modules. The algorithm considers each network in a population of neural networks as a potential solution to the optimal classification problem. Variables governing the learning and other parameters and network architecture are represented as substrings (genes) in a machine-level bit string (chromosome). When the population undergoes simulated evolution using genetic operators-selection based on a fitness function, crossover, and mutation-the average performance increases in successive generations. We found that, on the same data, compared with the best manually developed networks, evolved networks produced improved classifications in considerably less time, with no human effort, and with greater confidence in their optimality or near optimality. Strategies for devising a fitness function specific to the problem are explored and discussed.