Simulated annealing: theory and applications
Simulated annealing: theory and applications
Introduction to the theory of neural computation
Introduction to the theory of neural computation
Pattern classification with genetic algorithms
Pattern Recognition Letters - Special issue on genetic algorithms
Pattern classification with genetic algorithms: incorporation of chromosome differentiation
Pattern Recognition Letters
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multilayer perceptron, fuzzy sets, and classification
IEEE Transactions on Neural Networks
A stochastic learning-to-rank algorithm and its application to contextual advertising
Proceedings of the 20th international conference on World wide web
Optimization of process parameters in the abrasive waterjet machining using integrated SA-GA
Applied Soft Computing
Immunodomaince based Clonal Selection Clustering Algorithm
Applied Soft Computing
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A method is described for finding decision boundaries, approximated by piecewise linear segments, for classifying patterns in @?^N, N = 2, using Simulated Annealing (SA). It involves generation and placement of a set of hyperplanes (represented by strings) in the feature space that yields minimum misclassification. Theoretical analysis shows that as the size of the training data set approaches infinity, the boundary provided by the SA based classifier will approach the Bayes boundary. The effectiveness of the classification methodology, along with the generalization ability of the decision boundary, is demonstrated for both artificial data and real life data sets having non-linear/overlapping class boundaries. Results are compared extensively with those of the Bayes classifier, k-NN rule and multilayer perceptron, and Genetic Algorithms, another popular evolutionary technique. Empirical verification of the theoretical claim is also provided.