Journal ArticleDOI
Comparing backpropagation with a genetic algorithm for neural network training
TLDR
It is shown that the use of a genetic algorithm can provide better results for training a feedforward neural network than the traditional techniques of backpropagation.Abstract:
This article shows that the use of a genetic algorithm can provide better results for training a feedforward neural network than the traditional techniques of backpropagation. Using a chaotic time series as an illustration, we directly compare the genetic algorithm and backpropagation for effectiveness, ease-of-use, and efficiency for training neural networks.read more
Citations
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Journal ArticleDOI
Genetic algorithms approach to feature discretization in artificial neural networks for the prediction of stock price index
Kyoung-jae Kim,Ingoo Han +1 more
TL;DR: Genetic algorithms approach to feature discretization and the determination of connection weights for artificial neural networks (ANNs) to predict the stock price index is proposed.
Journal ArticleDOI
Optimizing connection weights in neural networks using the whale optimization algorithm
TL;DR: The qualitative and quantitative results prove that the proposed WOA-based trainer is able to outperform the current algorithms on the majority of datasets in terms of both local optima avoidance and convergence speed.
Journal ArticleDOI
An optimizing BP neural network algorithm based on genetic algorithm
TL;DR: A method that combines GA and BP to train the neural network works better; is less easily stuck in a local minimum; the trained network has a better generalization ability; and it has a good stabilization performance.
Journal ArticleDOI
Neural networks in business: techniques and applications for the operations researcher
TL;DR: An overview of the different types of neural network models which are applicable when solving business problems is presented, as well as their historical and current use in business.
References
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Book
Genetic Algorithms + Data Structures = Evolution Programs
TL;DR: GAs and Evolution Programs for Various Discrete Problems, a Hierarchy of Evolution Programs and Heuristics, and Conclusions.
Book
Handbook of Genetic Algorithms
TL;DR: This book sets out to explain what genetic algorithms are and how they can be used to solve real-world problems, and introduces the fundamental genetic algorithm (GA), and shows how the basic technique may be applied to a very simple numerical optimisation problem.
Journal ArticleDOI
Oscillation and Chaos in Physiological Control Systems
Michael C. Mackey,Leon Glass +1 more
TL;DR: First-order nonlinear differential-delay equations describing physiological control systems displaying a broad diversity of dynamical behavior including limit cycle oscillations, with a variety of wave forms, and apparently aperiodic or "chaotic" solutions are studied.
Journal ArticleDOI
Increased Rates of Convergence Through Learning Rate Adaptation
TL;DR: A study of Steepest Descent and an analysis of why it can be slow to converge and four heuristics for achieving faster rates of convergence are proposed.