Machine Vision: Theory, Algorithms, Practicalities
Machine Vision: Theory, Algorithms, Practicalities
Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series)
Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series)
Pattern Recognition, Third Edition
Pattern Recognition, Third Edition
Intelligent visual recognition and classification of cork tiles with neural networks
IEEE Transactions on Neural Networks
Evolutionary programming made faster
IEEE Transactions on Evolutionary Computation
A robust stochastic genetic algorithm (StGA) for global numerical optimization
IEEE Transactions on Evolutionary Computation
Corrections to “A Robust Stochastic Genetic Algorithm (StGA) for Global Numerical Optimization”
IEEE Transactions on Evolutionary Computation
A Pyramidal Neural Network For Visual Pattern Recognition
IEEE Transactions on Neural Networks
Neural Network Learning With Global Heuristic Search
IEEE Transactions on Neural Networks
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In this study, we critically analyse and compare performances of several global optimization (GO) approaches with our hybrid GLPτS method, which uses meta-heuristic rules and a local search in the final stage of finding a global solution. We also critically investigate a Stochastic Genetic Algorithm (StGA) method to demonstrate that there are some loopholes in its algorithm and assumptions. Subsequently, we employ the GLPτS method for neural network (NN) supervised learning, when using our intelligent system for solving real-world pattern recognition and classification problem. In the preprocessing data phase, our system also uses Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) for dimensionality reduction and minimization of the chosen number of features for the classification problem. Finally, the reported results are compared with Backpropagation (BP) to demonstrate the competitive properties and the efficiency of our system.