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Automatic Generation of Neural Network Architecture Using Evolutionary Computation

This book describes the application of evolutionary computation in the automatic generation of a neural network architecture. The architecture has a significant influence on the performance of the neural network. It is the usual practice to use trial and error to find a suitable neural network architecture for a given problem. The process of trial and error is not only time-consuming but may not generate an optimal network. The use of evolutionary computation is a step towards automation in neural network architecture generation.An overview of the field of evolutionary computation is presented, together with the biological background from which the field was inspired. The most commonly used approaches to a mathematical foundation of the field of genetic algorithms are given, as well as an overview of the hybridization between evolutionary computation and neural networks. Experiments on the implementation of automatic neural network generation using genetic programming and one using genetic algorithms are described, and the efficacy of genetic algorithms as a learning algorithm for a feedforward neural network is also investigated.

7.2 Evolutionary Computing to Analyse a NN Although this combination of GAs
and NNs is not common, GAs can be used to analyse or explain neural networks.
In [13] GAs are used as a neural network inversion tool in that they can find the ...

Soft Computing in Software Engineering

This book illustrates the impact of soft computing techniques on software engineering research and practices dealing with a range of novel methods reshaping the software development process. Specifically, it is shown how Software Engineering tasks such as reuse-oriented classification (e.g. components’ repositories), software diagnostic (e.g. bug detection and correction), effort prediction (e.g. project costs and time estimation), planning (e.g. project scheduling) and others can be appropriately handled by means of soft computing techniques. The book is a valuable reference for practitioners as well as an updated resource of ongoing interdisciplinary research in Soft Computing in Software Engineering.

Ernesto Damiani1, Lakhmi Jain2, and Mauro Madravio1 1 Department of
Information Technology University of Milan edamiam, mmadravio9crema.ux1in1i.
it 2 Knowledge-based Engineering Systems (KES) Center University of South
Australia ...