Hugo de Garis. Genetic programming: Building artificial nervous systems using genetically programmed neural network modules. In Proceedings of the 7th International Conference on Machine Learning, pages 132--139, ?, ? 1990. Morgan Kaufmann. y ga:deGaris90e.  Home/Search   Document Details and Download   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] ....

[Article contains additional citation context not shown here]

Hugo de Garis. Genetic programming: Building artificial nervous systems using genetically programmed neural network modules. In Proceedings of the 7th International Conference on Machine Learning, pages 132--139, ?, ? 1990. Morgan Kaufmann. y ga:deGaris90e.

....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] ....

[Article contains additional citation context not shown here]

Hugo de Garis. Genetic programming: Building artificial nervous systems using genetically programmed neural network modules. In Proceedings of the 7th International Conference on Machine Learning, pages 132--139, ?, ? 1990. Morgan Kaufmann. y ga:deGaris90e.

....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] ....

[Article contains additional citation context not shown here]

Hugo de Garis. Genetic programming: Building artificial nervous systems using genetically programmed neural network modules. In Proceedings of the 7th International Conference on Machine Learning, pages 132--139, ?, ? 1990. Morgan Kaufmann. y ga:deGaris90e.

....as synaptic weights and time constants are ideal low level primitives on which to apply artificial evolution. Similar considerations have led numerous researchers to combine neural networks and evolutionary algorithms for the control of animal like locomotion, in particular legged locomotion [9, 3, 35, 22, 31]. Except a few exceptions [32] these works, however, only considered the problem of pattern generation, without considering how to control these patterns for modulating the locomotion, namely the speed and the direction of movement. As mentioned earlier, this last point is one of the main ....

....the desired behaviour of the controller at a higher level, by determining desired characteristics of the swimming control in the mechanical simulation. These features make evolutionary algorithms a popular design technique in the field of neural networks applied to the control of animats (see [9, 3, 35, 22, 31] for evolutions of locomotion controllers and [15, 16] for a review of evolutions of behaviour controllers) Comparison with previous evolutions of swimming controllers for the lamprey In [26] we used a staged evolution approach to generate swimming controllers, with a first stage in which we ....

H. de Garis. Genetic programming: Building artificial nervous systems using genetically programmed neural network modules. In B.W. Porter and R.J. Mooney, editors, Proceedings of the seventh international conference on machine learning, pages 132-- 139. Morgan Kaufmann, 1990.

....very difficult to satisfy) the technique will fail unless a feasible point is introduced in the initial population [86] 6. 3 Behavioral memory Schoenauer and Xanthakis [122] proposed to extend a technique called behavioral memory, which was originally proposed for unconstrained optimization [30]. The main idea of this approach is that constraints are handled in a particular order. The algorithm is the following [122] ffl Start with a random population of individuals ffl Set j = 1 (j is the constraint counter) ffl Evolve this population to minimize the violation of the j th ....

Hugo de Garis. Genetic Programming: Building Artificial Nervous Systems using Genetically Programmed Neural Networks Modules. In R. Porter and B. Mooney, editors, Proceedings of the 7th International Conference on Machine Learning, pages 132--139. Morgan Kaufmann, 1990.

....during the evolution, the value of ff is increased by a factor of 10 to ensure the satisfaction of the constraints. Such ideas about iterated GAs has been demonstrated powerful in truss structure optimization, by Schoenauer and Xanthakis (1993) as well as in other domains of application (see de Garis (1990), Schoenauer (1994) All experiments presented in this paper are based on the penalized fitness, and involve this 2 step optimization. 4.3 First results Figure 3 show the first results on the 2 Theta 1 cantilever plate, discretized according to a 32 Theta 22 regular mesh. The population size ....

H. de Garis, 1990, "Genetic Programming: building artificial nervous systems using genetically programmed neural networks modules", in Proceedings of the 7 th International Conference on Machine Learning, R. Porter B. Mooney Eds, Morgan Kaufmann, 1990, pp 132-139.

.... for pursuit [Grefenstette 92] and navigation [Schultz 92] tasks; Husbands, Harvey, and Cliff have applied genetic algorithms to evolve neural controllers for robots using vision in simulated environments [Husbands 93] and De Garis has evolved neural networks for simulated legged locomotion [de Garis 90]. Previous research has demonstrated that a handwired DNN can control a simulated cockroach [Beer 90] The locomotion subnetwork from the simulated cockroach was transferred to a robot hexapod where it produced stable straight line gates [Chiel 92] Previous experiments have also shown that ....

....OE i is a binary variable representing whether the last two intervals between events for sensor i are sufficiently similar to trigger cycle detection. OE i is computed by: OE i = 1 if s i;t Gamma Deltat i and s i;t i and fl fl fl (t Gammae 1 ) e 1 Gammae 0 ) Gamma 1 fl fl fl k dev 0 otherwise where t is the current time, e 1 is the time of the previous event, e 0 is the time of the event that occurred prior to e 1 . k dev is a constant determining the maximum deviation in the ratio between the current event interval and the previous event interval. For example, if k dev is ....

[Article contains additional citation context not shown here]

Hugo de Garis, "Genetic Programming: Building Artificial Nervous Systems Using Genetically Programmed Neural Network Modules", Proceedings of the Seventh International Conference on Machine Learning.

....and to take the closest possible values in the stock. But it is well known that the discrete optimum might well be missed by such a simple strategy. 3.2 Constraints handling through behavioral memory 3.2. 1 The behavioral memory The behavioral memory paradigm, first introduced by De Garis [17], relies on the assumption that a population that has undergone artificial evolution contains more information than just the location of the point having the highest fitness: the localization of the whole population somehow witnesses the history of the population, how it did behave while evolving ....

....to the second fitness function. A common use of such iterated scheme amounts to gradually include more and more fitness cases in the computation of the fitness (e.g. more and more test points in regression problems) It has also been applied with completely different successive fitness functions [17, 53]. And it can be applied to handle constraints [52] as will be demonstrated in the following subsection. One of the key issues for such an iterated scheme is the genetic diversity: if the population that has evolved in the context of the first fitness function has converged, the bias induced by ....

de Garis, H., Genetic Programming: Building Artificial Nervous Systems using Genetically Programmed Neural Networks Modules, Proceedings of the 7th International Conference on Machine Learning, R. Porter and B. Mooney (Eds), Morgan Kaufmann, pp.132--139, 1990.

....depends on the other one, and a simultaneous optimization of both is a mandatory further step. Other on going work at CENA involves real scale simulations, before any real use can be imagined. 6 Behavioral Memory 6. 1 Motivations The Behavioral Memory paradigm, first introduced by De Garis [42], relies on the following claim: the set of solutions, given by the population evolved in a given landscape, contains more information than just one solution, even optimal. The localization of the last population somehow summarizes the history of the population, how it did behave while evolving ....

H. de Garis. Genetic programming : building artificial nervous systems using genetically programmed neural networks modules. In R. Porter and B. Mooney, editors, Proceedings of the 7 th International Conference on Machine Learning, pages 132--139. Morgan Kaufmann, 1990.

.... and the second stage effects a subsequent refinement by simulated annealing or stochastic gradient 1 Before its introduction to the graphics community, various incarnations of what we call controller synthesis had been proposed in the AI, robotics, and optimal control communities, e.g. [6, 9, 11, 15, 17, 22]. ascent. More recently, Sims [25] described a comparable approach to automatic motion synthesis as part of a system that generates both the structure of articulated figures and controllers that cause them to move in desired ways. In contrast with these approaches, Ngo and Marks employ a ....

H. de Garis. Genetic programming: Building artificial nervous systems using genetically programmed neural network modules. In Proceedings of the Seventh International Conference on Machine Learning, pages 132--139, Austin, Texas, June 1990.

....other genetic algorithms in that it allows for variable length genotypes, which allow for the dimensionality of the search space to be varied under evolutionary control. Other authors have explored the use of genetic algorithms in creating sensory motor controllers for adaptive behaviour (e.g. [1, 5]) but (as far as we are aware) all such work has involved using genetic search in a parameter space of fixed dimensionality: a relatively constrained optimisation task. For example, in [1] Beer and Gallagher use a genetic algorithm to find parameter values for a dynamical neural network ....

H. de Garis. Genetic programming: Building artificial nervous systems using genetically programmed neural network modules. In B. W. Porter and R. J. Mooney, editors, Proceedings of the Seventh International Conference on Machine Learning, pages 132--139. Morgan Kaufmann, 1990.

....network optimised with an adapted backpropagation algorithm [9] A recent AI technique to develop adapted controllers is evolving neural configurations using an Evolutionary Algorithm. This technique has been used successfully to develop walking controllers for hexapod agents [2] or biped agents [4], for instance. This paper examines the lamprey s swimming controller which has been studied extensively by Grillner and his colleagues [8] The mathematical model of the biological controller given in [5] is reproduced. Artificial controllers are created by using a Genetic Algorithm (GA) to ....

H. de Garis. Genetic programming: Building artificial nervous systems using genetically programmed neural network modules. In B.W. Porter and R.J. Mooney, editors, Proceedings of the seventh international conference on machine learning, pages 132-- 139. Morgan Kaufmann, 1990.

.... uniformly distributed individuals are fairly easy to draw in the most common representations frames (i.e. bitstrings and real valued parameters with prescribed bounds) It has been recognized, though, that a biased initialization using a preliminary EA run could greatly improve the overall results [10, 41]. Moreover, recent work in the GP framework [21] as well as the preliminary experiments of this paper, show that a careful choice of initialization procedure can enhance the performance of the further EA. It seems however, from the actual runs presented here, that such enhancement does not last ....

H. de Garis. Genetic programming : building artificial nervous systems using genetically programmed neural networks modules. In R. Porter and B. Mooney, editors, Proceedings of the 7 th International Conference on Machine Learning, pages 132--139. Morgan Kaufmann, 1990.

....for instance) But most of the works on GAs address the general optimization problem, rarely mentionning explicitely constrained problems. We present in this paper a general purpose technique for handling constraints in GA optimization processes. It is based on the notion of Behavioural Memory (de Garis 90) which takes into account the information contained in the whole population after some genetic evolution. The first steps of the whole process are devoted to just sampling the feasible region. The last step is then the genetic evolution of that sample, to optimize the final objective function. ....

....under genetic presssure can be viewed in a whole as a memory containing some essential information about the context it evolved in, that is the fitness function used in the GA. Such scheme, despite the fact that it has already been proved to be helpful on some difficult optimization problems (de Garis 90, Desquilbet 92) has not yet, as far as we know, been systematically applied to constrained optimization. In the simplest case, the whole optimization process is a two phases process: 0 1 2 3 4 5 0 1 2 3 4 5 B A a a b c d Constraint 1 Constraint 2 Maximum in A (0.10) Maximum in A (0.09) ....

H. de Garis, Genetic Programming : building artificial nervous systems using genetically programmed neural networks modules, in Proceedings of the 7 th International Conference on Machine Learning, R. Porter B. Mooney Eds, Morgan Kaufmann, 1990, pp 132-139.

....computed by our algorithm differ qualitatively from those that would be produced by existing local search techniques: they are complex, varied, multistaged, and sometimes far from obvious. Our work differs from previous global search and learning approaches to articulated figure motion control [6, 4, 12] in its adherence to physical law, the nature of the articulated figures being considered, and the generality of the problem statement, respectively. Developing an algorithm of this type requires numerous decisions regarding the design of mutation and crossover operators and the assignment of ....

H. de Garis. Genetic programming: Building artificial nervous systems using genetically programmed neural network modules. In Proceedings of the Seventh International Conference on Machine Learning, pages 132--139, Austin, Texas, June 1990.

No context found.

H. de Garis, "Genetic Programming : Building Artificial Nervous Systems Using Genetically Programmed Neural Network Modules", Proceedings 7th. Int. Conf. on Machine Learning, Austin Texas, June 1990, Morgan Kaufmann, 1990.

....: 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 Artificial Nervous Systems Using Genetically Programmed Neural Network Modules", Proceedings 7th. Int. Conf. on Machine Learning, Austin Texas, June 1990, Morgan Kaufmann, 1990.

No context found.

Hugo de Garis, "Genetic Programming: Building Artificial Nervous Systems Using Genetically Programmed Neural Network Modules", in Proc. 7th. Int. Conf. on Machine Learning, pp 132-139, Porter B.W. & Mooney R.J. (eds.), Morgan Kaufmann, 1990.

.... at self assembly do poorly How is progress in the design to be made when the self assembled device is massively complex The answer to this question may be to mimic nature by using a form of applied evolution called Genetic Programming (GP) i.e. using GAs to build evolve complex systems [de GARIS 1990, 1991a,b, 1992] Improvement in a hypercomplex system may be achieved blindly by randomly mutating linearly coded instructions for self assembling devices. Those mutations which are positive , will produce devices with superior performance values. The linear codes which contain these positive ....

....away from building hypercomplex machines, because of this un understandability , i.e. a lack of theoretical principles to explain their structures or functions. However, recently, a new approach to building (hyper) complex systems has been demonstrated. It is called Genetic Programming (GP) [de GARIS 1990, 1991a,b, 1992] which uses Genetic Algorithms (GAs) as a tool to build things, where the internal complexity of the system being built evolved is (within certain limits) irrelevant to its successful construction. So long as the GA being used gets a fitness value which continues to increase over ....

[Article contains additional citation context not shown here]

de Garis H. (1990), "Genetic Programming : Building Artificial Nervous Systems Using Genetically Programmed Neural Network Modules", in Porter B.W. & Mooney R.J. eds., Proc. 7th. Int. Conf. on Machine Learning, pp 132-139, Morgan Kaufmann.

No context found.

H. de Garis, "Genetic programming: Building artificial nervous systems using genetically programmed neural network modules," in Machine Learning: Proceedings of the Seventh International Conference (B. Porter and R. Mooney, eds.), pp. 132--139, San Francisco: Morgan Kaufmann, June 1990.

Online articles have much greater impact   More about CiteSeer.IST   Add search form to your site   Submit documents   Feedback  

CiteSeer.IST - Copyright Penn State and NEC