D.B. Fogel, E.C. Wasson, and E.M. Boughton, `Evolving neural networks for detecting breast cancer', Cancer Letters, 96, 49--53, (1995).  Home/Search   Document Not in Database   Summary   Related Articles   Check

....i.e. one objective is to map abnormal observations to a value close to 0278 0062 99 10.00 1999 IEEE one and the other objective is to map normal observations to a value close to zero. Genetic algorithms (GA s) 9] have been applied to many diagnostic and classification problems [8] [10] [15] A conventional GA, however, is a scalar optimization technique. It thus possesses the undesirable features of an aggregating based approach. One method of avoiding this is to adopt a multiobjective approach [16] 17] to the optimization problem. In a multiobjective optimization approach, ....

D. B. Fogel, E. C. Wasson III, and E. M. Boughton, "Evolving neural networks for detecting breast cancer," Cancer Lett., vol. 96, pp. 49--53, 1995.

....Keywords Pareto optimization, di#erential evolution, artificial neural networks, breast cancer. 1 Introduction The economic and social values of Breast Cancer Diagnosis (BCD) are very high. As a result, the problem has attracted many researchers in the area of computational intelligence recently [6, 8, 10, 22, 26, 32, 33, 34]. Because of the importance of achieving highly accurate classification, Artificial Neural Networks (ANNs) are among the most common methods for BCD. Research in the area of using ANNs for medical purposes more specifically BCD [6, 8, 10, 22, 26, 32, 34] has been at the center of attention ....

....intelligence recently [6, 8, 10, 22, 26, 32, 33, 34] Because of the importance of achieving highly accurate classification, Artificial Neural Networks (ANNs) are among the most common methods for BCD. Research in the area of using ANNs for medical purposes more specifically BCD [6, 8, 10, 22, 26, 32, 34] has been at the center of attention for several years. Unfortunately, to our present knowledge, none of this type of research was able to enter the clinic either in terms of routine use or to replace the radiologist. This could be ascribed to a number of factors. The first problem was the ....

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D.B. Fogel, E.C. Wasson, and E.M. Boughton. Evolving neural networks for detecting breast cancer. Cancer letters, 96(1):49--53, 1995.

....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 training algorithms is the ....

D.B. Fogel, E.C. Wasson, and E.M. Boughton. Evolving neural networks for detecting breast cancer. Cancer letters, 96(1):49--53, 1995.

....the concept of equivalent classes [115] 116] has given representations other than ary strings a more solid theoretical foundation. Real numbers have been proposed to represent connection weights directly, i.e. one real number per connection weight [27] 29] 30] 48] 63] 65] 74] 95] [96], 102] 110] 111] 117] 118] For example, a realnumber representation of the ANN given by Fig. 3(a) could be (4.0,10.0,2.0,0.0,7.0,3.0) As connection weights are represented by real numbers, each individual in an evolving population will be a real vector. Traditional binary crossover and ....

....mutation based EA s is that they can reduce the negative impact of the permutation problem. Hence the evolutionary process can be more efficient. There have been a number of successful examples of applying EP or ES to the evolution of ANN connection weights [29] 63] 65] 67] 68] 95] [96], 102] 106] 111] 117] 119] 120] In these examples, the primary search operator has been Gaussian mutation. Other mutation operators, such as Cauchy mutation [121] 122] can also be used. EP and ES also allow self adaptation of strategy parameters. Evolving connection weights by EP ....

D. B. Fogel, E. C. Wasson, and E. M. Boughton, "Evolving neural networks for detecting breast cancer," Cancer Lett., vol. 96, no. 1, pp. 49--53, 1995.

....[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] Bulletin of the Polish Academy of Sciences Chemistry, 100] Cancer Letters, [313] Chemometrics and Intelligent Laboratory Systems, 149, 495, 802] Chinese Journal of Advanced Software Research, 421] Complex Systems, 65, 786, 843, 845, 852, 874, 916] Comput. Ind. Eng. 387] Comput. Struct. UK) 382] Computer, 402] Computer Applications in the Biosciences (CABIOS) ....

....Christopher, 949, 953, 954] Bohari, Abdul Rahman, 434] Borges, Newton Chaves Kras, 156] Born, Joachim, 17, 102, 626, 627] Bornholdt, Stefan, 628, 629] 16 Genetic algorithms and neural networks Borst, Marko V. 18, 155] Bos, M. 630] Bossomaier, Terry, 202] Boughton, Edward M. [313] Boullart, L. 400] Bounds, David G. 631] Boyd, R. 632] Branke, Jurgen, 19, 157, 237] Brassinne, P. de, la, 633] Braun, H. 217] Braun, Heinrich, 20, 103, 435] Bressgott, W. 440] Brezina, T. 579] Brill, Frank Z. 634, 635] Brotherton, T. W. 158] Broughton, J. Q. 284] ....

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David B. Fogel, Eugene C. Wasson III, and Edward M. Boughton. Evolving neural networks for detecting breast cancer. Cancer Letters, 96(?):49--53, ? 1995. ga95hFogel.

....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 ....

....6 Gamma Gamma Gamma Gamma Gamma Gamma Delta Delta Delta Delta Delta Delta A A A A A AK 2 10 4 3 7 0010 0000 0100 1010 0011 0111 Figure 4: a) An ANN which is equivalent to that given in Figure 3(a) b) Its binary representation under the same representation scheme. connection weight [27, 29, 30, 48, 117, 63, 64, 65, 95, 96, 74, 111, 102, 110, 118]. For example, a realnumber representation of the ANN given by Figure 3(a) could be (4.0,10.0,2.0,0.0,7.0,3.0) As connection weights are represented by real numbers, each individual in an evolving population will be a real vector. Traditional binary crossover and mutation can no longer be used ....

[Article contains additional citation context not shown here]

D. B. Fogel, E. C. Wasson, and E. M. Boughton, "Evolving neural networks for detecting breast cancer," Cancer Letters, vol. 96, no. 1, pp. 49--53, 1995.

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D.B. Fogel, E.C. Wasson, and E.M. Boughton, `Evolving neural networks for detecting breast cancer', Cancer Letters, 96, 49--53, (1995).

No context found.

Fogel DB, Wasson 3rd EC, Boughton EM. Evolving neural networks for detecting breast cancer. Cancer Lett. 1995;96:49-53.

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