J. Fang and Y. Xi, "Neural Network design based on evolutionary programming," Artificial Intelligence in Engineering, vol. 11, pp. 155-- 161, 1997.  Home/Search   Document Not in Database   Summary   Related Articles   Check

.... algorithms (GAs) evolution strategies (ES) and particle swarm optimisation (PSO) GAs have previously be used for feature selection [8, 53] and topography selection [2, 5, 29, 35, 36, 38, 52] and ESs have been used for weight optimisation [21, 42, 45, 55] and adaptive topography selection [15, 37, 57]. The recent EC technique of PSO [27] has also proved popular as a uni objective NN optimiser [10, 12, 13, 26, 48] 2 Multi objective evolutionary neural network flamework The use of evolutionary approaches to NN training (with a single error function) has received increasing attention in recent ....

J. Fang and Y. Xi. Neural Network design based on evolutionary programming. Artificial Intelligence in Engineering, 11:155-161, 1997.

....Two different approaches have been taken in the direct encoding scheme. The first separates the evolution of architectures from that of connection weights [24] 150] 153] 154] 165] 167] 169] 170] The second approach evolves architectures and connection weights simultaneously [149] [179], 180] 182] 185] 200] This section will focus on the first approach. The second approach will be discussed in Section III D. In the first approach, each connection of an architecture is directly specified by its binary representation [24] 150] 153] 154] 165] 167] 169] 170] ....

....order their hidden nodes differently have two different genotypical representations, the probability of producing a highly fit offspring by recombining them is often very low. Some researchers thus avoided crossover and adopted only mutations in the evolution of architectures [45] 128] 149] [179], 185] 197] 217] 223] although it has been shown that crossover may be useful and important in increasing the efficiency of evolution for some problems [48] 113] 212] 229] Hancock [113] suggested that the permutation problem might not be as severe as had been supposed with the ....

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J. Fang and Y. Xi, "Neural network design based on evolutionary programming," Artificial Intell. Eng., vol. 11, no. 2, pp. 155--161, 1997.

....Advances in Applied Mathematics, 601] AI Expert, 64, 603, 644] AIAA Journal, 944] Analytica Chimica Acta, 548, 630] Appl. Intell. Int. Artif. Intell. Neural Netw. Complex Probl. Solving Technol. Netherlands) 467] Applied Mathematics and Computation, 590] Artif. Intell. Eng. UK) [424, 512] Artificial Life, 63, 301, 305, 556] Autom. Electr. Power Syst. China) 488] Bioinformatics, 588] Biological Cybernetics, 681, 835] Biophysical Journal, 126, 173] Bull. Fac. Eng. Univ. Tokushima (Japan) 336] Bull. Sci. Assoc. Ing. Electr. Inst. Electrotech. Montefiore, 633] ....

....[479] Erives, H. 334] Ersoy, O. K. 71, 484] Eshelman, Larry J. 907, 908, 957] Esparcia Alcazar, Anna J. 494] Esparcia Alc azar, Anna I. 443] Estevez, Pablo A. 34, 108, 168] Eyvazova, Z. E. 222] Fadda, A. 169, 335] Fagg, Andrew H. 804] Falcon, J. F. 674] Fang, Jian, [512] Farag, A. A. 547] Fariselli, Piero, 224, 558] Fekadu, Adhanom A. 761] Feldman, David S. 675] Ferguson, J. J. 676] Filelis, A. 871] Fleischhauer, T. 407] Fleming, Peter J. 589] Floreano, Dario, 677] Floyd, C. E. 160] Fogarty, Terence C. 241, 378, 436, 471, 474, 678] ....

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Jian Fang and Yugeng Xi. Neural network design based on evolutionary programming. Artif. Intell. Eng. (UK), 11(2):155--161, 1997. y(CCA 18790/97) ga97aJFang.

....Probl. Solving Technol (Netherlands) 1146] Appl. Intell. Int. J. Atif. Intell. Neural Netw. Complex Probl. Solving Technol. Netherlands) 1196] Appl. Math. Comput. USA) 861, 425] Applied Mathematics and Computation, 258] Arch. Control Sci. Poland) 538] Artif. Intell. Eng. UK) [189, 209, 289, 706] Asian Computer Weekly, 18] Autom. Electr. Power Syst. China) 165, 169, 173, 197, 218, 243, 247, 263] Autom. Electr. POwer Syst. China) 309] Autom. Electr. Power Syst. China) 363, 459] Biological Cybernetics, 642] Cailiao Kexue Yu Gongcheng, 407] Cailiao Yanjiu Xuebao, 405] ....

....[1082] Fahn, Chin Shyurng, 1119] Fahrner, W. R. 67] Falistagi, Abhijit, 524] Falkowski, B. 679] Fan, Alex, 112] Fan, Bo Tao, 445] Fan, Kuo Chin, 996, 1029, 1043, 1084, 1104, 1132, 1150] Fang, D. G. 267, 360, 402] Fang, H. L. 1176] Fang, Hsiao Lan, 1057, 1093] Fang, Jian, [289] Fang, Kangling, 324] Fang, Liu, 415] Fang, S. C. 182] Fang, Shu Cherng, 270] Fang, W. G. 278] Fanlun, Xiong, 132] Fei, Z. Y. 148] Feng, Cheng Min, 1200] Feng, Ma, 327, 412] Feng, N. N. 402] Feng, S. 118] Feng, Shan, 1142] Feng, Shyuan Ming, 1046] Feng, Yuncheng, ....

[Article contains additional citation context not shown here]

Jian Fang and Yugeng Xi. Neural network design based on evolutionary programming. Artif. Intell. Eng. (UK), 11(2):155-161, 1997. yCCA 18790/97 ga97aJFang.

....parents from the population based on their fitness. 5. Apply search operators to the parents and generate offspring which form the next generation. Figure 6: A typical cycle of the evolution of architectures. Considerable research on evolving ANN architectures has been carried out in recent years [33, 42, 45, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 149, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 138, 213, 214, 215, 216, 118, 130, 127, 217, 218, 219, 220, 221, 222, 223, 128, 224, 225]. Most of the research has concentrated on the evolution of ANN topological structures. Relatively little has been done on the evolution of node transfer functions, let al..one the simultaneous evolution of both topological structures and node transfer functions. In this paper, we will analyze the ....

....Encoding Scheme Two different approaches have been taken in the direct encoding scheme. The first separates the evolution of architectures from that of connection weights [154, 153, 150, 24, 170, 169, 165, 167] The second approach evolves architectures and connection weights simultaneously [179, 180, 182, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 149, 198, 199, 200]. This section will focus on the first approach. The second approach will be discussed in Section 3.4. In the first approach, each connection of an architecture is directly specified by its binary representation [154, 153, 150, 24, 170, 169, 165, 167, 202] For example, an N Theta N matrix C = c ....

[Article contains additional citation context not shown here]

J. Fang and Y. Xi, "Neural network design based on evolutionary programming," Artificial Intelligence in Engineering, vol. 11, no. 2, pp. 155--161, 1997.

.... models (Art networks, self organizing feature maps, see (Cotrell, Girard, Rouset 1997) ffl Other approaches e.g. alternative neuron models (see (Burg Tschichold Gurman 1997) ffl Evolutionary algorithms which can be used for training as well as for determining the topology of ANNs (see (Fang Xi 1997)) Multilayer Perceptrons (MLPs) MLPs come about through the joining together of multiple non linear perceptrons (see (Haykin 1999) and are multilayered feedforward networks. Figure 1 shows the formal representation of a single neuron used in MLPs, consisting of an input, an activation, and an ....

Fang, J., and Xi, Y. 1997. Neural Network Design Based on Evolutionary Programming. Artificial Intelligence in Engineering 11:155--161.

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J. Fang and Y. Xi, "Neural Network design based on evolutionary programming," Artificial Intelligence in Engineering, vol. 11, pp. 155-- 161, 1997.

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