Optimization of Neural Network Parameter using Genetic Algorithm
Extraction of Neural Network Weights using Genetic Algorithm Based Back propagation Network
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Genetic Algorithms are one of the most powerful techniques in optimization and search problems. The Objective was, to understand this powerful technique and to explain it in better way so as to expand its field of application.The algorithm is so versatile that it can be used in any field.The objective of the GA is to find an optimal solution to a problem. Since Gas is heuristic procedures, they are not guaranteed to find the optimal solution, but they are able to find very good solutions for a wide range of problems.The use of both, genetic algorithms and artificial neural networks, was origin...
Genetic Algorithms are one of the most powerful techniques in optimization and search problems. The Objective was, to understand this powerful technique and to explain it in better way so as to expand its field of application.The algorithm is so versatile that it can be used in any field.The objective of the GA is to find an optimal solution to a problem. Since Gas is heuristic procedures, they are not guaranteed to find the optimal solution, but they are able to find very good solutions for a wide range of problems.The use of both, genetic algorithms and artificial neural networks, was originally motivated by the astonishing success of these concepts in there biological counterparts.Despite their totally deferent approaches, both can merely be seen as optimization methods, which are used in a wide range of applications.They are capable to finding solution to hard NP-based Problems. Neural Networks utilizing back propagation based learning have promisingly showed results to a vast variety of function and problems. TSP is one such classical problem for theoretical computation.
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