D.J. Janson and J.F. Frenzel. Application of genetic algorithms to the training of higher order neural networks. Systems Engineering, 2:272--276, 1992.  Home/Search   Document Not in Database   Summary   Related Articles   Check

....to adopt in a changing environment. In the literature, research into EANN has been taking one of three approaches; evolving the weights of the network, evolving the architecture, or evolving both simultaneously. The EANN approach uses either binary representation to evolve the weight matrix [12, 13] or real [7, 8, 9, 18, 19, 23] There is not an obvious advantage of binary encoding in EANN over the real. However, with real encoding, there are more advantages including compact and natural representation. The key problem (other than being trapped in a local minimum) with BP and other ....

D.J. Janson and J.F. Frenzel. Application of genetic algorithms to the training of higher order neural networks. Systems Engineering, 2:272--276, 1992.

....to adopt in a changing environment. In the literature, research into EANN has been taking one of three approaches; evolving the weights of the network, evolving the architecture, or evolving both simultaneously. The EANN approach uses either binary representation to evolve the weight matrix [10, 11] or real [6, 7, 16, 19] There is not an obvious advantage of binary encoding in EANN over the real. However, with real encoding, there are more advantages including compact and natural representation. The key problem (other than being trapped in a local minimum) with BP and other traditional ....

D.J. Janson and J.F. Frenzel. Application of genetic algorithms to the training of higher order neural networks. Systems Engineering, 2:272--276, 1992.

....often termed chromosomes. Some of the early work in evolving ANN connection weights followed this approach Fig. 3. a) An ANN with connection weights shown. b) A binary representation of the weights, assuming that each weight is represented by four bits. 24] 26] 28] 37] 38] 41] [52], 53] In such a representation scheme, each connection weight is represented by a number of bits with certain length. An ANN is encoded by concatenation of all the connection weights of the network in the chromosome. A heuristic concerning the order of the concatenation is to put connection ....

....(or fitness) function and thus is particularly appealing when this information is unavailable or very costly to obtain or estimate. For example, the evolutionary approach has been used to train recurrent ANN s [41] 60] 65] 100] 102] 103] 106] 117] 126] 128] higher order ANN s [52], 53] and fuzzy ANN s [76] 77] 129] 130] Moreover, the same EA can be used to train many different networks regardless of whether they are feedforward, recurrent, or higher order ANN s. The general applicability of the evolutionary approach saves a lot of human efforts in developing ....

D. J. Janson and J. F. Frenzel, "Application of genetic algorithms to the training of higher order neural networks," J. Syst. Eng., vol. 2, pp. 272--276, 1992.

....multiple error measures. A method of error smoothing is also introduced as an attempt to solve the current stopping problem associated with ES (and GA) trained NNs. The use of Evolutionary and Genetic approaches to Neural Network training has received increasing attention in recent years [4, 5, 6, 7, 9, 12, 14]. Indeed the ability of these approaches to facilitate NN training beyond the Euclidean objective was highlighted by Porto et al. [9] but apparently taken no further. Other limited approaches to multi objective training do appear in the literature, but in the form of simultaneously choosing the ....

Janson, D.J. and Frenzel, J.F., "Application of Genetic Algorithms to the training of Higher Order Neural Networks", Journal of Systems Engineering, Vol. 2, No. 4, 1992

.... of Applied Physiology, 583] Journal of Japanese Society for Artificial Intelligence, 791] Journal of Mathematical Biology, 810] Journal of Medicinal Chemistry, 394, 461] Journal of Microcomputer Applications, 288, 668] Journal of Qing Hua University, 277] Journal of Systems Engineering, [696] Journal of Technical Physics (Poland) 300] Journal of the Society of Instrument and Control Engineers, 140] Lancet, 332] Machine Learning, 959] Mem. Tokohu Inst. Technol. I, Sci. Eng. Japan) 565] Methods of Information in Medicine, 901] Midwest Symp Circuits Syst, 273] ....

....Fogel, Lawrence J. 276, 681, 685, 687, 691] Foo, Shou King, 170] Forst, C. V. 248] Fortuna, L. 639, 640] Foy, Mark, 693] Foy, M. 694] Franco, Aurali B. 888] Fredriksson, Kimmo, 496, 517] Freedman, M. T. 487] Freisleben, Bernd, 695] French, I. G. 171] Frenzel, James F. [696, 697, 698, 699] Friedrich, Ch. M. 497] Fu, Chi Yung, 240] Fujii, T. 498] Fujimoto, Yoshiji, 561] Fujita, S. 36] Fukuda, Toshio, 147, 249, 257, 294, 310, 552, 569, 700, 701, 702, 703, 704, 705, 706, 707, 708] Fukumi, M. 336, 444, 466] Fukumi, Minoru, 709, 710] Fullmer, Brad, 838] Funabiki, ....

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David J. Janson and James F. Frenzel. Application of genetic algorithms to the training of higher order neural networks. Journal of Systems Engineering, 2(4):272--276, 1992. y(CCA 25393/93) ga:Frenzel92a.

.... for Artificial Intelligence, 385, 398, 710] Journal of Korean Institute of Telematics and Electronics, 283] Journal of Modeling, Measurement and Control, C, 196] Journal of Molecular Structure, 565] Journal of Structural Engineering ASCE, 356, 576] Journal of Systems Engineering, [220] JSPP, 471] KI Kunstliche Intelligenz, 74] Knowledge Based Systems (UK) 537] Kybernetes, 239] Lettre du Transputer et des Calculateurs Distribu es, 657] Machine Learning, 88] Mathematical and Computer Modelling, 241, 339] Memoirs of the Faculty of Engineering, Fukui University, ....

....Fonseca, Carlos M. 193] Fontain, Eric, 212, 213] Fontana, Walter, 214] Forrest, Stephanie, 215, 216, 217, 218, 564] Fortuna, L. 117] Foy, Mark D. 264] Frank, P. 120] Frankhauser, Pierre, 219] Frazer, J. H. 345] Freeman, L. M. 375] Freeman, Ray, 565] Frenzel, James F. [220, 221] Freund, Harald, 222] Freyer, Stephan, 223] Frieder, Ophir, 635] Fukuda, Toshio, 224, 225, 226, 227, 228, 229, 230, 231] Fullmer, Brad, 496] Furst, M. 532] Furuhashi, Takeshi, 674] Furuya, Tatsumi, 719] Gall, A. Le, 182] Gallagher, John C. 85] Galletly, J. E. 239] Gammack, ....

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David J. Janson and James F. Frenzel. Application of genetic algorithms to the training of higher order neural networks. Journal of Systems Engineering, 2(4):272--276, 1992. y(CCA 25393/93) ga:Frenzel92a.

....or even continuous since EAs do not depend on gradient information. Because EAs can treat large, complex, nondifferentiable and multimodal spaces, which are the typical case in the real world, considerable research and application has been conducted on the evolution of connection weights [24, 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]. The evolutionary approach to weight training in ANNs consists of two major phases. The first phase is to decide the representation of connection weights, i.e. whether in the form of binary strings or not. The second one is the evolutionary process simulated by an EA, in which search operators ....

....Figure 2: A typical cycle of the evolution of connection weights. 2.1 Binary Representation The canonical GA [13, 14] has always used binary strings to encode alternative solutions, often termed chromosomes. Some of the early work in evolving ANN connection weights followed this approach [24, 26, 28, 37, 38, 41, 52, 53]. In such a representation scheme, each connection weight is represented by a number of bits with certain length. An ANN is encoded by concatenation of all the connection weights of the network in the chromosome. A heuristic concerning the order of the concatenation is to put connection weights to ....

[Article contains additional citation context not shown here]

D. J. Janson and J. F. Frenzel, "Application of genetic algorithms to the training of higher order neural networks," Journal of Systems Engineering, vol. 2, pp. 272--276, 1992.

....since GAs do not depend on gradient information in search. Because GAs are good at dealing with large, complex, nondifferentiable and multimodal spaces which are the typical space defined by an error function or fitness function, a lot of work has been done on the evolution of connection weights [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]. The evolutionary approach to weight training in EANNs consists of two major stages. The first stage is to decide the genotype representation of connection weights, i.e. whether in the form of binary strings or not. The second one is the evolution itself simulated by a GA or other evolutionary ....

....weights. Reprinted with permission from Ref. 1. X. Yao: Evolutionary Artificial Neural Networks 9 2. 1 Binary Representation Since the binary representation has been shown to be beneficial in GA s search [4, 5] one way to represent connection weights is to encode them in binary strings [29, 30, 32, 41, 42, 45, 56, 57]. In such a representation scheme, each connection weight is represented by a number of binary bits with certain length. An EANN is represented by concatenation of all the connection weights in the network. A heuristic concerning the order of the concatenation is to put connection weights to the ....

[Article contains additional citation context not shown here]

D. J. Janson and J. F. Frenzel. Application of genetic algorithms to the training of higher order neural networks. Journal of Systems Engineering, 2:272--276, 1992.

....its fitness. 4. Apply genetic operators, such as crossover and mutation, to each child individual generated above and obtain the next generation. Figure 1: A typical cycle of the evolution of connection weights. fitness function, a lot of work has been done on the evolution of connection weights [27, 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]. The evolution of connection weights provides an alternative approach to training EANNs. Such an evolutionary approach consists of two major stages. The first stage is to decide the genotype representation of connection weights, i.e. whether in the form of binary strings or not. The second one ....

....evolution of connection weights is shown in Figure 1. X. Yao: Evolutionary Artificial Neural Networks 5 2. 1 Binary Representation Since the binary representation has been shown to be beneficial in GA s search [11, 12] one way to represent connection weights is to encode them in binary strings [27, 29, 31, 40, 41, 44, 55]. In such a representation scheme, each connection weight is represented by a number of binary bits with certain length. For example, Whitley et al. 27, 29] used 8 bits to represent each connection weight, which ranges between Gamma127 and 127, in their experiments with XOR and adder ....

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D. J. Janson and J. F. Frenzel. Application of genetic algorithms to the training of higher order neural networks. Journal of Systems Engineering, 2:272--276, 1992. X. Yao: Evolutionary Artificial Neural Networks 39

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D. J. Janson and J. F. Frenzel. Application of genetic algorithms to the training of higher order neural networks. Journal of Systems Engineering, 2(4):272--276, 1992.

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D.J. Janson and J.F. Frenzel. Application of Genetic Algorithms to the Training of Higher Order Neural Networks. Journal of Systems Engineering, 2(4):272--276, 1992.

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