Hugo de Garis. Genetic programming: Building nanobrains with genetically programmed neural network module. In 1990 International Joint Conference on Neural Networks - IJCNN 90, volume 3, pages 511--516, San Diego, CA, 17.-21. June 1990. IEEE, New York. * ga:deGaris90c.  Home/Search   Document Not in Database   Summary   Related Articles   Check

....[91] Galbiati, R. 321] Gali c, Elvis, 337] Gallagher, John C. 611, 612] Gallagher, N. B. 149] Gant, V. 332] Gao, Xinbo, 289] Garcia, E. 350] Gardner, Julian W. 761] Gargano, Michael L. 788] Garis, Hugo de, 99, 138, 140, 233] Garis, Hugo De, 490] Garis, Hugo de, [523, 970, 971, 972, 973, 974, 975, 976, 977, 978, 979, 980, 981, 982, 983, 984, 985, 986, 987, 988, 989, 990, 991] Gaudet, Charles, 338] Gayko, Jens E. 511] Geerestein, V. J. 425] Gelsema, E. S. 174, 339] George, R. 244] George, Suju M. 110] Georgiopoulos, Michael, 152, 432] Gers, Felix, 523] Gers, F. 501] Geyer, Claudio Fernando R. 156] Gherrity, Michael, 613] Ghosh, Ashish, ....

.... deterministic, 466] dynamic, 295] neural Darwinism, 556] neural netoworks, 243] neural network, 539] control, 280, 515] design, 444] fuzzy, 423, 477] rule extraction, 464] signal processing, 287] structure selection, 172] training, 354] wavelet, 281] neural networks, [619, 665, 666, 914, 939, 599, 600, 650, 651, 727, 783, 799, 945, 946, 601, 606, 613, 642, 649, 656, 657, 711, 728, 729, 854, 909, 947, 948, 949, 950, 951, 952, 970, 602, 607, 610, 615, 634, 637, 645, 652, 663, 670, 678, 679, 680, 681, 682, 683, 684, 685, 714, 721, 730, 756, 757, 762, 764, 770, 781, 782, 785, 786, 787, 800, 810, 815, 834, 869, 874, 875, 879, 885, 902, 904, 907, 910, 920, 923, 940, 953, 954, 971, 973, 974, 975, 976, 977, 608, 618, 620, 622, 628, 630, 644, 654, 660, 661, 664, 674, 686, 687, 688, 693, 712, 713, 731, 735, 758, 763, 793, 796, 806, 813, 817, 841, 857, 859, 860, 861, 864, 886, 889, 890, 911, 913, 915, 921, 936, 942, 955, 956, 960, 972, 978, 979, 980, 981, 982, 592, 594, 595, 605, 611, 614, 616, 621, 635, 636, 638, 639, 643, 658, 669, 671, 672, 673, 689, 690, 691, 696, 700, 716, 717, 719, 720, 722, 723, 724, 725, 726, 733, 734, 736, 737, 738, 739, 740, 741, 766, 767, 773, 774, 778, 784, 790, 791, 795, 797, 801, 804, 809, 814, 818, 819, 821, 822, 823, 824, 829, 835, 836, 837, 838, 845, 846, 858, 871, 876, 880, 881, 900, 906, 908, 917, 919, 922, 925, 926, 941, 957, 958, 965, 967, 968, 983, 984, 593, 596, 597, 598, 609, 612, 617, 629, 631, 632, 633, 640, 641, 646, 653, 659, 662, 668, 675, 677, 692, 694, 698, 701, 703, 704, 705, 706, 709, 742, 743, 744, 745, 746, 747, 748, 749, 750, 751, 752, 753, 761, 765, 768, 769, 771, 775, 776, 780, 788, 794, 802, 803, 805, 807, 808, 811, 812, 825, 827, 832, 833, 842, 844, 847, 848, 849, 850, 856, 862, 863, 865, 866, 868, 870, 872, 873, 877, 884, 887, 892, 893, 894, 895, 896, 901, 905, 918, 927, 929, 930, 931, 933, 934, 935, 937, 938, 943, 944, 961, 963, 966, 985, 986, 987, 988, 13, 14, 18, 26, 38, 40, 43, 63, 75, 84, 86, 88, 91, 96, 99, 103, 106, 108, 111, 112, 113, 114, 118, 121, 124, 125, 128, 129, 134, 137, 138, 139, 140, 146, 147, 151, 153, 156, 157, 166, 167, 169, 180, 190, 191, 192, 193, 196, 199, 200, 209, 215, 217, 222, 226, 227, 230, 236, 238, 241, 242, 244, 254, 258, 260, 264, 266, 268, 269, 271, 272, 277, 279, 283, 292, 295, 296, 297, 303, 305, 318, 321, 322, 323, 327, 333, 338, 349, 353, 366, 372, 374, 375, 378, 381, 388, 390, 398, 400, 403, 404, 407, 408, 411, 415, 427, 440, 441, 448, 453, 454, 456, 458, 459, 463, 473, 360, 475, 478, 481, 487, 489, 490, 491, 492, 493, 495, 499, 504, 505, 508, 512, 523, 525, 526, 530, 531, 533, 542, 556, 562, 566, 583] neural networks age, 206] analysis, 362] architecture, 41, 184] associative memory, 570] back propagation, 397] back propagation, 502] backpropagation, 64, 69, 115, 351, 447] Baldwin effect, 155] Bayesian, 174, 339, 527] binary logic, 565] biological, 779] Boltzmann, 70] ....

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Hugo de Garis. Genetic programming: Building nanobrains with genetically programmed neural network module. In 1990 International Joint Conference on Neural Networks - IJCNN 90, volume 3, pages 511--516, San Diego, CA, 17.-21. June 1990. IEEE, New York. * ga:deGaris90c.

....Gambardella, Luca M. 982] Gamberger, D. 462] Gamble, Rose, 304] Gane, C. 337] Gang, Rong, 587] Gantes, Charis, 248] Gao, Chunhua, 139] Gao, Yong, 879] Garces Perez, Jaime, 244] Garcia, E. 359] Garcia, Ephrahim, 121, 596] Garcia Contreras, E. L. 245] Garis, Hugo de, [1472, 1473, 1474, 1475, 1476, 1477, 1478] Garvey, S. D. 1004] Garza, A. G. de Silva, 1270] Gaspar, Alessio, 140, 965] Gaspersic, Janez, 246] Gathercole, Chris, 247] Gaudenzi, Paolo, 248] Gaul, A. J. 1006] Gauthier, F. O. 564] Geary, R. A. 1362] Gebert, Glenn A. 37, 924] Gelenbe, Erol, 249] Gelsey, Adrew, ....

.... adaptive, 315] directed, 1009] genome dependence, 709] neutral, 260] navigation collision avoidance, 872] helmsman, 755] nesting, 99, 606, 1012] 3D, 344] network partitioning, 1228] neural network fuzzy, 884] rule extraction, 1174] training, 389] neural networks, [1329, 1331, 1334, 1338, 1341, 1345, 1361, 1370, 1372, 1375, 1377, 1378, 1379, 1390, 1391, 1392, 1394, 1396, 1399, 1402, 1408, 1417, 1422, 1437, 1439, 1440, 1448, 1451, 1452, 1455, 1469, 1472, 1473, 1475, 1476, 1478, 55, 122, 131, 160, 199, 347, 383, 492, 597, 608, 634, 695, 730, 749, 750, 832, 928, 983, 985, 1033, 1097, 1125, 1163] neural networks analysis, 455] back propagation, 692] backpropagation, 367] cellular, 176, 777] classification, 835] coding, 278] control, 789, 864] design, 359, 712, 1047] diagnosis, 742] fault detection, 159] feedforward, 496] fuzzy, 367, 1057, 1127] hardware, 543] ....

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Hugo de Garis. Genetic programming: Building nanobrains with genetically programmed neural network module. In 1990 International Joint Conference on Neural Networks - IJCNN 90, volume 3, pages 511--516, San Diego, CA, 17.-21. June 1990. IEEE, New York. * ga:deGaris90c.

....89, 90, 91] Fogel, David B. 92, 93, 94, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107] Fogel, Lawrence J. 95, 97, 98, 104, 105, 106] Fontana, Walter, 108] Forrest, Stephanie, 109, 110] Frazer, L. N. 192] Freeman, L. M. 169, 170] Galarce, Carlos E. 114] Garis, Hugo de, [303, 304, 305, 306, 307, 308, 309, 310] Geary, R. A. 81] Gerys, D. 280] Glesner, M. 115] Goldberg, David E. 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 167] Goldberg, Yaron, 60] Gorges Schleuter, Martina, 130, 129] Gottvald, A. 131] 12 Genetic algorithms of 1990 Greenwood, Daniel, 233] ....

....video tape, 273] VLSI, 50] CAD, 255] layout design, 173] VLSI design, 270, 271] Walsh functions, 121] water tank, 281] welding, 73] word processing, 77] Geographical index 17 4.8 Annual index The following table gives references to the contributions published annually. 1990, [13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 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, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299, 300, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310] 1991, 8, 9, 10, 11] 1992, 12] 4.9 Geographical index The following table gives references to the contributions by country. ffl Australia: 282] ffl Austria: 145, 205, 302] ffl Belgium: 276, 303, 304, 305, 306, 307, 308, 309, 310] ffl Canada: 75, 76, 263] ffl Czech Republic: 131] ....

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Hugo de Garis. Genetic programming: Building nanobrains with genetically programmed neural network module. In 1990 International Joint Conference on Neural Networks - IJCNN 90, volume 3, pages 511--516, San Diego, CA, 17.-21. June 1990. IEEE, New York. * ga:deGaris90c.

....obtained networks with half the training error of that obtained by BP, but which 22 also took twice as long as BP. Maniezzo [47] obtained more accurate networks than those obtained with BP by using a variable length encoding for each weight. Santos and Duro [65] Fukuda et al. 19] and de Garis [14, 15] have evolved recurrent neural networks successfully. In particular, Fukuda et al. were able to obtain better results than the BPTT algorithm. The framework developed by de Garis enables large networks to be progressively trained by first developing smaller networks for specific tasks, and then ....

....the rule) having class 11. The TRUE child specifies wall pushing should be used in case there is a neuron already to the east of the initial neuron, but since no neuron is there this is ignored. Finally, rule 13 is the only CHANGE ACTIVATION rule present in the individual. It changes 73 1. AC (CXT[1, 2, 3, 4, 6, 7, 10, 14, 15]) 0, 4, 5, 6, 8, 10, 14, 15] FALSE (input) 2. AC (CXT[0, 1, 2, 4, 8, 9, 10, 14, 15] 1, 3, 5, 6, 7, 9, 10, 11, 12, 13, 15, 16] TRUE (output) 3. AC (CXT[2, 3, 4, 9, 10, 13, 14, 16, 17] 0, 2, 3, 5, 6, 8, 9, 10, 13, 14, 16, 17] TRUE (output) 4. AC (CXT[0, 3, 5, 6, 7, 11, 12, 16, 17] 0, 1, ....

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Hugo de Garis. Genetic programming: Building nanobrains with genetically programmed neural network modules. In IJCNN'90: International Joint Conference on Neural Networks, volume 3, pages 511--516, San Diego, CA, 17--21 June 1990. IEEE Press.

....eventually converging to a program for f [6, 10] A class of functions S is Ex identifiable just in case there is a machine that Ex identifies each member of S. Here is a particularly simple example of a self referential coding trick. Let SD = f com 1 In other empirical work (for example, [42, 43, 41, 11, 12, 13, 16, 40, 39]) one pre trains on a succession of prior tasks to achieve success on a current task. Mastery of previous tasks provides useful context for the next. One sees similar attempts in animal training by shaping desired behavior through a succession of approximations [21, 15] e.g. to teach a dog to ....

H. de Garis. Genetic programming: Building nanobrains with genetically programmed neural network modules. In IJCNN: International Joint Conference on Neural Networks, volume 3, pages 511--516. IEEE Service Center, Piscataway, New Jersey, June 17--21, 1990.

....that perform well in a given environment. If a neural network is used to encapsulate a particular behaviour, then genetic algorithms can be used to evolve that behaviour, by evolving a population of neural networks. One particular approach to the evolution of behaviour is described by de Garis [1]. In this approach, a GA is used to evolve a population of neural networks. Each NN has a set of adjustable weights and is used to encapsulate some desired behaviour (e.g. walking) In other words, once good weights have been found, the NN can be used by itself to perform the desired behaviour. ....

....is not known in advance, they must be learned. Instead of the more traditional NN learning algorithms (e.g. backpropagation) de Garis uses a genetic algorithm to learn a set of good weights. No learning is being done by the neural network itself. This approach is called genetic programming [1]. As mentioned above, GAs evolve a population of individuals according to the process of natural selection. During this process, genetic operators create new (child) individuals from highly fit old (parent) individuals. Recombination (also referred to as crossover in this report) is one of the ....

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de Garis, H. (1990a). Genetic Programming: Building Nanobrains with Genetically Programmed Neural Network Modules, Proceedings of the International Joint Conference on Neural Networks, San Diego, CA, June 1990.

....Rodney Neural Firings Figure 1: The Experimental Setup Maes [12] has created a self organizing system for the control of a walking insect. However, it is not immediately clear how these systems can be scaled for the design of hypercomplex systems. Immediately relevant is the work of de Garis [6]. He has applied Genetic Algorithms to the design of neural network for control of simulated bipedal walking machines. De Garis introduced the idea of Behavioral Memory. The key observation is that the evolution of a controller can be affected by the starting conditions of the search. It is ....

Hugo de Garis. Genetic Programming : Building Nanobrains with Genetically Programmed Neural Network Modules. In Proceedings of the International Joint Conference on Neural Neetworks, July 1990.

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H. de Garis, "Genetic Programming : Building Nanobrains with Genetically Programmed Neural Network Modules", Proceedings IJCNN90 SanDiego, International Joint Conference on Neural Networks, June1990, SanDiego.

....: The LIZZY Project 9. Future Ideas for The LIZZY Project 10. Genetic Programming (GP) of Embryos 11. Future Embryo Ideas : Electronic Neuro Embryology 12. References 1. Introduction This article introduces the concept of Genetic Programming in a more general way than in earlier papers [2,3,4,5]. Genetic Programming (GP) is the application of the Genetic Algorithm [7,8] to the creation of systems which are too complex in their dynamics to be analyzed or pre specified in detail. Such systems can be built, but (probably) not understood. Two major applications of GP will be introduced in ....

....Neural Networks and Artificial Life. Many Lifers (Artificial Life researchers) hope that their bottom up behavioural approach and the top down disembodied symbolic approach of AI researchers will one day meet somewhere in the middle. 10. Genetic Programming (GP) of Embryos Earlier papers [2,3,4,5] defined Genetic Programming (GP) as a new programming methodology which used the Genetic Algorithm to evolve neural network modules. However, the ideas of this section and the next will show that the GP approach is quite general. For example, it can be applied to the evolution of artificial ....

H. de Garis, "Genetic Programming : Building Nanobrains with Genetically Programmed Neural Network Modules", Proceedings IJCNN90 SanDiego, International Joint Conference on Neural Networks, June1990, SanDiego.

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Hugo de Garis. Genetic Programming: Building Nanobrains with Genetically Programmed Neural Network Modules. CADEPS AI Research Unit, Universitye Libre de Bruxelles, CP 194/7, B-1050 Brussels, Belgium, 1990.

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