Evolutionary Neural Network Learning

TL;DR: This work presents two hybrid EC/ANN algorithms: the first evolves neural topologies while the latter performs simultaneous optimization of architectures and weights.

TL;DR: This letter investigates a geometrical method to optimize the kernel function, a modification of the one proposed by S. Amari and S. Wu, that works efficiently and overcomes the susceptibility of the original method.

TL;DR: In this paper, a spiking neuroevolutionary system which implements memristors as plastic connections is presented, i.e. whose weights can vary during a trial, allowing the evolution of networks of appropriate complexity to emerge whilst exploiting the memristive properties of the connections to reduce learning time.

TL;DR: GAs and Evolution Programs for Various Discrete Problems, a Hierarchy of Evolution Programs and Heuristics, and Conclusions.

Abstract: Abstract This book provides the first comprehensive treatment of feed-forward neural networks from the perspective of statistical pattern recognition. After introducing the basic concepts of pattern recognition, the book describes techniques for modelling probability density functions, and discusses the properties and relative merits of the multi-layer perceptron and radial basis function network models. It also motivates the use of various forms of error functions, and reviews the principal algorithms for error function minimization. As well as providing a detailed discussion of learning and generalization in neural networks, the book also covers the important topics of data processing, feature extraction, and prior knowledge. The book concludes with an extensive treatment of Bayesian techniques and their applications to neural networks.

TL;DR: It is shown, through a considerably large literature review, that combinations between ANNs and EAs can lead to significantly better intelligent systems than relying on ANNs or EAs alone.