Introduction to the theory of neural computation
Introduction to the theory of neural computation
Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Genetic algorithms + data structures = evolution programs (2nd, extended ed.)
Genetic algorithms + data structures = evolution programs (2nd, extended ed.)
Artificial intelligence: a new synthesis
Artificial intelligence: a new synthesis
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Efficient Hierarchical Parallel Genetic Algorithms using Grid computing
Future Generation Computer Systems
Applying Mathematica and webMathematica to graph coloring
Future Generation Computer Systems
Artificial Bee Colony (ABC) Optimization Algorithm for Training Feed-Forward Neural Networks
MDAI '07 Proceedings of the 4th international conference on Modeling Decisions for Artificial Intelligence
ISNN '08 Proceedings of the 5th international symposium on Neural Networks: Advances in Neural Networks, Part II
Real-coded genetic algorithm for parametric modelling of a TRMS
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Evolving neural network for printed circuit board sales forecasting
Expert Systems with Applications: An International Journal
Novel classification and segmentation techniques with application to remotely sensed images
Transactions on rough sets VII
The development of a weighted evolving fuzzy neural network
ICIC'06 Proceedings of the 2006 international conference on Intelligent computing: Part II
An efficient hierarchical parallel genetic algorithm for graph coloring problem
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Evolving neural networks using the hybrid of ant colony optimization and BP algorithms
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part I
A new crossover for solving constraint satisfaction problems
EvoCOP'13 Proceedings of the 13th European conference on Evolutionary Computation in Combinatorial Optimization
A hierarchical parallel genetic approach for the graph coloring problem
Applied Intelligence
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This paper investigates the effectiveness of the genetic algorithm (GA) evolved neural network classifier and its application to the land cover classification of remotely sensed multispectral imagery. First, the key issues of the algorithm and the general procedures are described in detail. Our methodology adopts a real coded GA strategy and hybrid with a back propagation (BP) algorithm. The genetic operators are carefully designed to optimize the neural network, avoiding premature convergence and permutation problems. Second, a SPOT-4 XS imagery is employed to evaluate its accuracy. Traditional classification algorithms, such as maximum likelihood classifier, back propagation neural network classifier, are also involved for a comparison purpose. Based on an evaluation of the user's accuracy and kappa statistic of different classifiers, the superiority of applying the discussed genetic algorithm-based classifier for simple land cover classification using multispectral imagery data is established. Thirdly, a more complicate experiment on CBERS (China-Brazil Earth Resources Satellite) data and discussion also demonstrates that carefully designed genetic algorithm-based neural network outperforms than gradient descent-based neural network. This has been supported by the analysis of the changes of connection weights and biases of the neural network. Finally, some concluding remarks and suggestions are also presented.