David B. Fogel , Eugene C. Wasson , Edward M. Boughton and Vincent W. Porto, A step toward computer-assisted mammography using evolutionary programming and neural networks, Cancer Letters, Volume 119, Issue 1, pp. 93-97, 1997.  Home/Search   Document Not in Database   Summary   Related Articles   Check

....et al. 22] presented a comparison between the data envelopment analysis and ANN. They found that the ANN approach was significantly better than the data envelopment analysis approach with around 25 improvement in the classification accuracy. In sum, all previous methods, except Fogel et al. [8, 9], depended on the conventional BP algorithm which can easily be trapped in a local minimum and requires extensive computational time to find the best number of hidden units. The approach of Fogel et al. 8, 9] presented a successful attempt to use evolutionary computations for solving the problem. ....

....in the classification accuracy. In sum, all previous methods, except Fogel et al. 8, 9] depended on the conventional BP algorithm which can easily be trapped in a local minimum and requires extensive computational time to find the best number of hidden units. The approach of Fogel et al. [8, 9] presented a successful attempt to use evolutionary computations for solving the problem. Although the approach achieved better predictive accuracy than the others, it su#ered from its high computational cost and dependance on either knowing the right number of hidden units in advance or ....

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D.B. Fogel, E.C. Wasson, and V.W. Porto. A step toward computer-assisted mammography using evolutionary programming and neural networks. Cancer letters, 119(1):93, 1997.

....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 V.W. Porto. A step toward computer-assisted mammography using evolutionary programming and neural networks. Cancer letters, 119(1):93, 1997.

....on 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 ....

....of using 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 ....

D. B. Fogel, E. C. Wasson, and V. W. Porto, "A step toward computer-assisted mammography using evolutionary programming and neural networks," Cancer Lett., vol. 119, no. 1, p. 93, 1997.

....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 ....

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D. B. Fogel, E. C. Wasson, and V. W. Porto, "A step toward computer-assisted mammography using evolutionary programming and neural networks," Cancer Letters, vol. 119, no. 1, p. 93, 1997.

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Fogel, D.B., Wasson, E.C., Boughton, E.M., and Porto, V.W.: A step toward computer -assisted mammography using evolutionary programming and neural networks. Cancer Lett. (1997) in press.

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David B. Fogel , Eugene C. Wasson , Edward M. Boughton and Vincent W. Porto, A step toward computer-assisted mammography using evolutionary programming and neural networks, Cancer Letters, Volume 119, Issue 1, pp. 93-97, 1997.

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