Computer Science and Information Engineering, World Congress on (2009)
Los Angeles, California USA
Mar. 31, 2009 to Apr. 2, 2009
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/CSIE.2009.471
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.
Artificial Neural Network, MEA, Optimization Design, Structure Optimization
Tao Fan, Ruiping Wen, "MEA for Designing Neural Network Weights and Structure Optimization", Computer Science and Information Engineering, World Congress on, vol. 06, no. , pp. 111-115, 2009, doi:10.1109/CSIE.2009.471