Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations
Practical neural network recipes in C++
Practical neural network recipes in C++
Machine learning: neural networks, genetic algorithms, and fuzzy systems
Machine learning: neural networks, genetic algorithms, and fuzzy systems
Refining a neural network credit application vetting system with a genetic algorithm
Journal of Microcomputer Applications
Global Optimization for Neural Network Training
Computer - Special issue: neural computing: companion issue to Spring 1996 IEEE Computational Science & Engineering
Simultaneous design and training of ontogenic neural network classifiers
Computers and Operations Research - Special issue: artificial intelligence, evolutionary programming and operations research
Discretisation in Lazy Learning Algorithms
Artificial Intelligence Review - Special issue on lazy learning
Genetic Algorithms and Investment Strategies
Genetic Algorithms and Investment Strategies
Evolving the Topology and the Weights of Neural Networks Using a Dual Representation
Applied Intelligence
Variable Hidden Layer Sizing in Elman Recurrent Neuro-Evolution
Applied Intelligence
Neural Nets Trained by Genetic Algorithms for Collision Avoidance
Applied Intelligence
Feature Transformation and Subset Selection
IEEE Intelligent Systems
Feature Subset Selection Using a Genetic Algorithm
IEEE Intelligent Systems
Feature Space Transformation Using Genetic Algorithms
IEEE Intelligent Systems
Forming Categories in Exploratory Data Analysis and Data Mining
IDA '97 Proceedings of the Second International Symposium on Advances in Intelligent Data Analysis, Reasoning about Data
Computers and Operations Research - Special issue: Emerging economics
Evolutionary Radial Basis Functions for Credit Assessment
Applied Intelligence
Employee turnover: a neural network solution
Computers and Operations Research
Neural Networks in Finance: Gaining Predictive Edge in the Market (Academic Press Advanced Finance Series)
Training feedforward neural networks using genetic algorithms
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 1
Hybrid learning using genetic algorithms and decision trees for pattern classification
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
Journal of Systems and Software
Shell-neighbor method and its application in missing data imputation
Applied Intelligence
A new evolutionary system for evolving artificial neural networks
IEEE Transactions on Neural Networks
Tuning of the structure and parameters of a neural network using an improved genetic algorithm
IEEE Transactions on Neural Networks
Genetic evolution of the topology and weight distribution of neural networks
IEEE Transactions on Neural Networks
Using a case-based reasoning approach for trading in sports betting markets
Applied Intelligence
Strategies for avoiding preference profiling in agent-based e-commerce environments
Applied Intelligence
A belief classification rule for imprecise data
Applied Intelligence
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Artificial neural networks (ANNs) have been popularly applied for stock market prediction, since they offer superlative learning ability. However, they often result in inconsistent and unpredictable performance in the prediction of noisy financial data due to the problems of determining factors involved in design. Prior studies have suggested genetic algorithm (GA) to mitigate the problems, but most of them are designed to optimize only one or two architectural factors of ANN. With this background, the paper presents a global optimization approach of ANN to predict the stock price index. In this study, GA optimizes multiple architectural factors and feature transformations of ANN to relieve the limitations of the conventional backpropagation algorithm synergistically. Experiments show our proposed model outperforms conventional approaches in the prediction of the stock price index.