Machine learning, neural and statistical classification
Machine learning, neural and statistical classification
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Algorithms for Graphics and Imag
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Pattern Recognition and Neural Networks
A New Diploid Scheme and Dominance Change Mechanism for Non-Stationary Function Optimization
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AICT'11 Proceedings of the 2nd international conference on Applied informatics and computing theory
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This paper describes application of Genetic algorithm (GA) for design of network configuration and for learning of neural network. Design of network configuration is the first area for GA exercise in relation to neural network. The number of neurons in network and placement to the layers has big influence over effectivity of whole system. If we are able to formulate quality classification of designed network from standpoint of topology, we can use GA for design of suitable network configuration. The second area (learning of neural network) consists in using of advantages of GA toward learning of neural networks. In this case GA looks for acceptable setting of network weights so, to make specified transformation - it practices minimalization of its mistake function. The Genetic algorithm is considered to be a stochastic heuristic (or meta-heuristic) method. Genetic algorithms are inspired by adaptive and evolutionary mechanisms of live organisms. The best use of Genetic algorithm can be found in solving multidimensional optimisation problems, for which analytical solutions are unknown (or extremely complex) and efficient numerical methods are also not known.