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Los Angeles, CA
March 31, 2009 to April 2, 2009
ISBN: 978-0-7695-3507-4
pp: 111-115
ABSTRACT
For Artificial Neural Network application, its weights and structure optimization design is a key problem. The Mind Evolutionary Algorithm (MEA) is a new evolutionary algorithm which simulates the process of human mind evolution, it has the powerful ability to find global optimum, and it also has much superiority for resolving the problem of numerical and non-numerical optimization. In this paper, a new typical MEA is presented based on the foundational MEA framework to optimize the neural network structure and weights, in which effective similartaxis and dissimilation operators of structure optimization are designed. Through similartaxis operators, the local optimum is found, then exceeding the restriction of local range through dissimilation operators, the global optimum is acquire in global solution space. Finally, simulation results show the effectiveness and correctness of the method.
INDEX TERMS
Artificial Neural Network, MEA, Optimization Design, Structure Optimization
CITATION
Tao Fan, Ruiping Wen, "MEA for Designing Neural Network Weights and Structure Optimization", CSIE, 2009, 2009 WRI World Congress on Computer Science and Information Engineering, CSIE, 2009 WRI World Congress on Computer Science and Information Engineering, CSIE 2009, pp. 111-115, doi:10.1109/CSIE.2009.471
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