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Proceedings ArticleDOI

Learning of neural networks with GA-based instance selection

25 Jul 2001-Vol. 4, pp 2102-2107
TL;DR: This paper examines the effect of instance and feature selection on the generalization ability of trained neural networks through computer simulations on various artificial and real-world pattern classification problems.
Abstract: We examine the effect of instance and feature selection on the generalization ability of trained neural networks for pattern classification problems. Before the learning of neural networks, a genetic-algorithm-based instance and feature selection method is applied for reducing the size of training data. Nearest neighbor classification is used for evaluating the classification ability of subsets of training data in instance and feature selection. Neural networks are trained by the selected subset (i.e., reduced training data). In this paper, we first explain our GA-based instance and feature selection method. Then we examine the effect of instance and feature selection on the generalization ability of trained neural networks through computer simulations on various artificial and real-world pattern classification problems.

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Journal ArticleDOI
TL;DR: This work shows how instance selection algorithms for boosting can be combined advantageously to produce better and simpler ensembles than random subspace method (RSM) method for k-NN and standard ensemble methods for C4.5 and SVMs.
Abstract: In this paper, we approach the problem of constructing ensembles of classifiers from the point of view of instance selection. Instance selection is aimed at obtaining a subset of the instances available for training capable of achieving, at least, the same performance as the whole training set. In this way, instance selection algorithms try to keep the performance of the classifiers while reducing the number of instances in the training set. Meanwhile, boosting methods construct an ensemble of classifiers iteratively focusing each new member on the most difficult instances by means of a biased distribution of the training instances. In this work, we show how these two methodologies can be combined advantageously. We can use instance selection algorithms for boosting using as objective to optimize the training error weighted by the biased distribution of the instances given by the boosting method. Our method can be considered as boosting by instance selection. Instance selection has mostly been developed and used for k -nearest neighbor (k -NN) classifiers. So, as a first step, our methodology is suited to construct ensembles of k -NN classifiers. Constructing ensembles of classifiers by means of instance selection has the important feature of reducing the space complexity of the final ensemble as only a subset of the instances is selected for each classifier. However, the methodology is not restricted to k-NN classifier. Other classifiers, such as decision trees and support vector machines (SVMs), may also benefit from a smaller training set, as they produce simpler classifiers if an instance selection algorithm is performed before training. In the experimental section, we show that the proposed approach is able to produce better and simpler ensembles than random subspace method (RSM) method for k-NN and standard ensemble methods for C4.5 and SVMs.

101 citations


Cites methods from "Learning of neural networks with GA..."

  • ...[45] showed that instance selection based on nearest neighbors can be used to improve the performance and reduce training time of a neural network....

    [...]

Journal ArticleDOI
TL;DR: A survey on the application of Evolutionary Algorithms to Instance Selection and Generation process will cover approaches applied to the enhancement of the nearest neighbor rule, as well as other approaches focused on the improvement of the models extracted by some well-known data mining algorithms.
Abstract: The use of Evolutionary Algorithms to perform data reduction tasks has become an effective approach to improve the performance of data mining algorithms. Many proposals in the literature have shown that Evolutionary Algorithms obtain excellent results in their application as Instance Selection and Instance Generation procedures. The purpose of this paper is to present a survey on the application of Evolutionary Algorithms to Instance Selection and Generation process. It will cover approaches applied to the enhancement of the nearest neighbor rule, as well as other approaches focused on the improvement of the models extracted by some well-known data mining algorithms. Furthermore, some proposals developed to tackle two emerging problems in data mining, Scaling Up and Imbalance Data Sets, also are reviewed.

72 citations

Journal ArticleDOI
TL;DR: A new application of the concept of mutual information not to select the variables but to decide which prototypes should belong to the training data set in regression problems to focus in prototype selection for regression problems.

44 citations


Cites background or methods from "Learning of neural networks with GA..."

  • ...The majority of the research in outlier/instance/prototype selection has been focused in classification problems [7,8], although few works aimed at solving problems for continuous output....

    [...]

  • ...Recently, evolutionary algorithms [7], boosting-based algorithms [14], and pruning techniques [15] have also been applied to this problem....

    [...]

Journal ArticleDOI
TL;DR: The results obtained from the data sets from NN5 competition in time series prediction indicate that the proposed method increases the quality of long-term time series Prediction, as well as reduces the amount of instances needed for building the model.

43 citations


Additional excerpts

  • ...All rights reserved....

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01 Jan 2012

37 citations


Additional excerpts

  • ...H, [585℄Kuiper, Herman, [874℄Kukreja, Basant, [40℄Kumagai, T., [740, 832℄Kumagai, Totu, [633, 680℄Kumano, H., [632℄Kumar, A., [810℄Kumar, K. K., [66℄Kun heva, L., [377℄Kun heva, Ludmila I., [580℄Kundu, Malay K., [1153℄Kung, C. H., [710℄Kung, C. M., [710℄Kunze, M., [259℄Kuo, L. E., [67℄Kuo, R. J., [21℄Kupfermann, I., [1029℄Kuriyama, Y., [726℄Kuroda, Chiaki, [378, 581℄Ku s u, _Ibrahim, [68℄Kussul, Ernst M., [203℄Kwa sni ka, Halina, [669℄Kwasni ka, H., [276℄Kwiatkowski, Laurent, [1179℄Kwiesielewi z, M., [582℄Kwon, Jangwoo, [713℄Kwong, S., [235℄Kyng as, Jari, [359, 379℄Kyng as, J., [475℄Kyyr o, J., [359℄La hiver, G., [273℄Lagaros, N. D., [757℄Lai, Loi Lei, [69, 742℄Lai, W. K., [1047℄Laitinen, Teija, [333, 451℄Lam, D. C., [364℄Lambert-Torres, G., [784℄Lampinen, J., [630℄Lampinen, Jouni, [838℄Land, Walker, [532, 672℄Langenhove, L. Van, [418℄ Langholz, Gideon, [70, 110℄Langley, A. 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A., [616℄Ristov, Strahil, [365℄Ritter, Helge, [61℄Robbins, Philip, [1144℄Roberts, Stephen G., [221℄Robillard, C., [488℄Ro ha, Armando Freitas da, [1222℄Rogers, David, [902℄Rogers, Leah Lu ille, [1045℄Romaniuk, Steve G., [89, 132, 222,292, 401, 1145, 1146, 1147℄Ronald, Edmund, [97, 143, 1164℄Ronald, E., [523, 662℄Rooij, A. J. F. Van, [499℄Rosa, A., [753℄Rosen, S. R., [1029℄Rosewarne, Brendan S., [530℄Roska, Tam as, [897℄Ross, J., [1125℄Rossi, C., [376℄Routen, Tom, [133℄Rouvinen, A., [617℄Rowe, Jon, [179℄Rowlands, H., [402℄Rubin, Stuart H., [1148℄Rudni k, William Mi hael, [1149,1150, 1151℄Rudolph, S., [403℄Rudy, George, [780℄Russell, Je rey S., [825℄Russo, Fabrizio, [13, 531℄Russo, M., [750℄Rutkowska, D., [802℄Ryu, D. H., [343℄Saad, D., [1168℄Sagara, Setsuo, [687℄Sagiroglu, S., [175℄Sagrario S an hez, M., [622℄Saha, Swapan, [93℄Sakasai, K., [374℄Sakihara, H., [778℄Salama, Rameri, [16, 228℄Sal i , Z. A., [596℄ Salomon, Ralf, [678℄Samad, Tariq, [977, 978,979, 980, 981, 982, 983, 984, 1155℄San hez, Elie, [917℄San hez, E., [432℄San his, A., [564, 813℄Sandoval, F., [312, 529,1156, 1157℄Sang, Kim Chong, [127℄Sanjeevan, K., [345℄Sankaranasayanan, V., [265, 356℄Sano, Chiharu, [1158℄Santib a~nez-Koref, Ivan, [29, 114, 875,876℄Santos, Antonino, [395℄Santos, J., [44, 94℄Sanz-Gonzalez, Jose, [99℄Sarabia, Luis A., [622℄Sarat handran, P., [182℄Saravanan, N., [95, 296℄Sarkar, M., [590, 671℄Sarmiento, A., [44℄Sasaki, M., [729℄Sasaki, W., [836℄Sase, M., [413℄Satalino, G., [770℄Sato, Y., [229, 443℄Sato, Yuji, [96, 414℄Satomi, K., [735℄Saunders, Gregory M., [147℄Saxena, Ashutosh, [123℄S h afer, J., [230℄S ha er, J. David, [1159, 1160,1210℄S herer, A., [415℄S hi mann, Wolfram, [1135, 1161,1162, 1163℄S hirp, G., [637℄S hizas, C. N., [1087℄S hizas, Christos N., [394℄S hlageter, G., [415℄S hlenzig, J., [936, 939℄S hme k, Hartmut, [31, 169, 202℄S hme k, Heinri h, [1120℄ 28 Geneti algorithms and neural networksS hmidt, M., [297, 416℄S hmitz, G. P. J., [765℄S hneider, A., [298℄S hneider, Gisbert, [50, 122, 138,185, 264℄S hoenauer, Mar , [143, 1164℄S hoenauer, M., [97℄S hoenaur, Mar , [351℄S hoenaur, M., [181℄S holz, M., [1165℄S hraudolph, Ni ol N., [863, 864, 865℄S hu hhardt, Johannes, [122, 185, 264℄S hultz, A., [98℄S hwaiger, Roland, [192, 387, 624℄Sebald, A. V., [929, 933,934, 936, 939, 940℄Sebastian, P., [808℄Se hi, G. R., [703℄Segovia, Javier, [625℄Segovia, J., [766℄Seijas, Juan, [99℄Selige, Thomas, [268℄Selige, T., [469℄Seliger, R., [767℄Selman, Bart, [1166℄Selvage, John E., [709℄Selvam, M. A. P., [434℄Sendho , Bernhard, [560, 626℄Senjyu, T., [778℄Seo, Jae-Yong, [741℄Seongwon, Cho, [127℄Sere, Kaisa, [160, 333, 451℄Sergeev, S. A., [417, 768℄Serra, R., [899, 1167℄Sette, S., [418℄Sexton, R. S., [760℄Sexton, Randall S., [15, 830℄Shaheen, Samir I., [550℄Shamir, N., [1168℄Shams, S., [85℄Shang, Yi, [420℄Shao, H. H., [496℄Sharif, A. M., [811℄ Sharman, K. C., [191℄Sharman, Ken C., [464℄Sharman, Ken, [528℄Sharp, David H., [848, 849, 850℄Shavlik, Jude W., [84℄Sheble, Gerald B., [71℄Shi, Chunyi, [654℄Shi, Y., [327℄Shibata, Takanori, [271, 681, 950,951, 952, 953, 954, 955, 957, 958℄Shih, Ching Ching, [712℄Shimohara, Katsunori, [100℄Shimohara, K., [807℄Shimojima, Joji, [664℄Shimojima, Koji, [159, 309, 325℄Shin, Chulkyu, [713℄Shin, Jin-Ho, [199℄Shin, Seong-Hyo, [764℄Shine, J. A., [194℄Shinke, Noboru, [752℄Shirao, Yoshiaki, [59℄Shonkwiler, Ronald, [1169℄Shukla, K. K., [697℄Siemon, H. P., [1013℄Sierra, B., [670℄Sigurdsson, H. S., [510℄Silva, A., [606℄Silva, N., [753℄Silva, Val eres V. R., [782℄Sim, Kwee-Bo, [716, 724℄Sim, Siang-Kok, [599℄Simoes, Eduardo do Valle, [307℄Simpson, P. K., [170℄Sin, Sam-Kit, [1170℄Singh, Kirti, [123℄Sittisathan hai, Sin hai, [101, 905℄Siu, Wan Chi, [819℄Skinner, A. J., [299℄Sklansky, Ja k, [421, 605℄Sklar, Elizabeth, [702℄Smalz, R. W., [398℄Smalz, Robert, [102℄ Smith, A. E., [174℄Smith, Ali e E., [176, 486℄Smith, D. J., [1027℄Smith, Je , [1028℄Smith, L. S., [1046℄Smith, R. E., [611℄Smith, Roger, [829℄Smith, Teren e R., [1126℄Smolander, S., [630℄Smuda, Ellen, [66℄Snoad, Nigel, [215℄So, Sung-Sau, [412, 482, 700℄Sobotka, M., [1171℄ Sojdr, Martin, [231, 422℄Sole, I., [345℄Solidum, Alan, [1054℄Soltys, James R., [470℄Song, Jing, [1004, 1005℄Song, Renguo, [759℄Song, Y. H., [650℄Song, Yong-Hua, [792℄Song, Yoon-Seon, [612℄Soper, Alan, [1144℄Spasi , Z. A., [218℄Spears, William M., [1172, 1173℄Spiessens, Piet, [1174℄Spittle, Mark C., [1015℄Spo ord, J. J., [1012℄Sprinkhuizen-Kuyper, Ida G., [167℄Spron k, P., [567℄Srikanth, R., [258℄Srikumar, Rangarajan, [80℄Srinivas, M., [1114℄Srinivasan, D., [719℄Srivastava, A. K., [697℄Srivastava, S. K., [697℄Sta ey, Deborah A., [1043℄Stafylopatis, A., [769℄Stan, I., [347℄Starkweather, Timothy John, [1203,1207℄Staszewski, W. J., [423℄ Authors 29Stathaki, A., [828℄Steele, Nigel C., [1140, 1141,1142, 1143℄Steenstrup, Martha, [103℄Stender, Joa him, [1175℄Stepniewski, Slawomir W., [424℄Sternieri, A., [770℄Stidsen, T., [297℄Sto ker, E., [504℄Stojmenovi , I., [698℄Stonham, T. J., [855℄Storey, a. M., [364℄Stork, David G., [1176, 1177,1178℄Stramaglia, S., [770℄Stratton, T. R., [232℄Stri ker, R., [425℄Stromboni, Jean-Paul, [1179℄Su, D., [426℄Su, Fong-Chin, [22℄Sugai, Y., [275℄Sugimoto, Okamoto J., [1180℄Sugimoto, Y., [81℄Sugiyama, K., [62℄Summers, R., [242℄Sun, Chengyi, [520℄Sun, Yan, [520℄Sundararajan, N., [182℄Susu, Yao, [444℄Suykens, Johan, [1187℄Suzuki, J., [634, 673℄Suzuki, Tatsuya, [233℄Sveinsson, J. R., [510℄Swayne, D. A., [364℄Syed, Omar, [216℄Tadel, M., [912℄Taha, Mahmoud A., [825℄Takagawara, Y., [603℄Takagi, Hideyuki, [1053, 1183,1184℄Takahashi, H., [427, 1185℄Takahashi, K., [836℄Takano, Takeshi, [270℄ Takeda, F., [317, 799℄Takeda, Fumiaki, [104, 134, 428℄Takefuji, Y., [774℄Takeu hi, Jun, [429℄Takuma, Masanori, [752℄Tanaka, Kazuo, [369℄Tanaka, K., [729℄Tanaka, Masahiro, [129℄Tanaka, Toshio, [1239℄Tanaka, Toshiyuki, [300℄Tang, K. S., [235℄Tang, XiaoXiao, [647℄Tanie, Kazuo, [681, 953,954, 955℄Tanino, Tetsuzo, [129℄Tanprasert, T., [588℄Taraglio, S., [430℄Taylor, C., [75℄Taylor, David, [851℄Tazaki, Eii hiro, [270℄Tekeu hi, T., [1181℄Teller, Astro, [12, 1186℄Teo, Ming-Yeong, [599℄Terada, Kengo, [134℄Terada, K., [104℄Teramati, Y., [632℄Terao, H., [602℄Terekhin, A. T., [116℄Teunis, M., [589℄Themlin, Jean-Mar , [289℄Thierens, Dirk, [1187℄Thompson, A. C., [242℄Thompson, Wiley E., [209, 280, 302℄Thornton, Chris, [68℄Tibbetts, C., [367℄Tilley, David G., [1106℄Timofeyev, A. V., [431℄Todd, Peter M., [961, 962, 963℄Todorova, L., [377℄Tohyama, Hisao, [834℄Tomera, M., [582℄Tomilinson, G. R., [109℄ Tomlinson, G. R., [200, 423℄Tong, David W., [1050℄Top hy, A. P., [502, 636, 659℄Topping, B. H. V., [682℄Torreele, Jan, [1174, 1189℄Toth, G. J., [408℄T oth, G abor J., [1190, 1191℄Tourassi, G. D., [172℄Touretzky, David S., [1192℄Trint, K., [106℄Troya, Jos e M., [844, 845, 847℄Tsai, Du-Yih, [717℄Tsang, Chi Ping, [867℄Tselioudis, G., [607℄Tseng, Ching-Shiow, [252℄Tsinas, Lampros, [105℄Tsompanakis, Y., [757℄Tsoukalas, Lefteri H., [586, 683℄Tsuji, Teruo, [687℄Tsujii, O., [516℄Tsutsui, H., [540, 691, 693℄Tsutsui, Shigeyoshi, [649℄Tsutsumi, K., [763℄Tsutsumi, Yasuhiro, [159℄Turega, Mike, [221℄Tzes, Anthony, [60, 491℄U hikawa, Yoshiki, [552, 779℄U hikawa, Y., [691, 775℄Uezato, K., [778℄Ugur, A., [155℄Uhrig, Robert E., [204, 683℄Uhrik, C., [944℄UI hida, H., [836℄Ults h, A., [1013℄Usher, A., [1026℄Uthmann, Thomas, [1127, 1128℄Utre ht, U., [106℄Utsugi, A., [832℄Va htsevanos, George J., [481℄Vahidov, M. A., [236℄Vahidov, R. M., [236℄Valastro, G., [899℄ 30 Geneti algorithms and neural networksValen ia, S. San hez, [312℄Valenzuela, Christine L., [855℄Valjakka, J., [475℄Vandewalle, Joos, [1187℄Van Belle, Terry, [638℄Vann, P. A., [151℄Vansteenkiste, G., [135℄Vdovi hev, S., [738℄Veelenturf, L. P. J., [239, 301℄Veenker, Gerd, [1195℄Velas o, Juan R., [592℄Veloso, Manuela, [12℄Ventura, Dan, [237℄Vermeers h, L., [135℄Vers hure, Paul F. M. J., [1196℄Vi o, F. J., [1156℄Viharos, Z. J., [639℄Villani, Mar o, [238℄Vitale, J. N., [607℄Vivarelli, Fran es o, [238, 640℄Vla havas, I., [586℄Vladimirova, T., [503, 660℄Voigt, Hans-Mi hael, [29, 875, 876℄Voln a, Eva, [641, 720℄Voln a, Evo, [433℄Vonk, E., [239, 301℄Voronenko, D. I., [783℄Voronovsky, G. K., [768℄Voss, Heiko, [560℄Vriesenga, Mark, [421℄Vukobratovi , Miomir, [718℄Waagen, Don E., [130℄Waagen, Don, [73, 1072,1073, 1075, 1076, 1077, 1078℄Wada, Mitsuo, [633, 680℄Wada, M., [740, 832℄Wah, Benjamin W., [420℄Wahab, Ashraf H Abdel, [550℄Wahidabanu, R. S. D., [434℄Walker, S. G., [293℄Walker, S ott, [1177℄Wallrafen, J., [435℄ Wamlook, Rustom, [302℄Wang, D. D., [341℄Wang, Dazhong, [642℄Wang, Fangju, [117℄Wang, J. W., [731℄Wang, Ke-Jun, [822℄Wang, Q., [496℄Wang, X. F., [69℄Wang, Xiufeng, [441℄Wanrooij, E. van, [107℄Ware, Andrew, [744℄Ware, J. A., [556℄Warsi, N., [258℄Warwi k, Kevin, [1030℄Wasson III, Eugene C., [328℄Watanabe, Y., [789℄Watson, Mark, [207℄Watta, P. B., [436℄Watts, M. J., [572℄Watts, M., [751℄Wazlawi k, Raul Sidnei, [240℄Weber, H. T., [879℄Weber, J., [262℄Weeks, E. R., [526℄Wehenkel, L., [241℄Wehrens, Ron, [19℄Wei, C. J., [295℄Weijer, A. P. de, [1052℄Wei , Gerhard, [108, 136, 964℄Weiss, K. R., [1029℄Weller, P. R., [242℄Weller, P., [348℄Wen, Xu, [645℄Wenhua, Xu, [536℄Wenhui, Chen, [519℄Werner, R., [1135, 1163℄Wesolkowski, Slawomir, [643℄Westland, S., [1026℄Westphal, H., [437℄Wezel, Mi hiel C. van, [160, 546℄Whitaker, Kevin W., [1197℄White, David W., [39℄ White, D., [903℄Whitehead, B. A., [438℄Whitehead, Bru e A., [439℄Whitfort, T., [566, 644℄Whitley, Darrell L., [357℄Whitley, Darrell, [144, 318,329, 1160, 1198, 1199, 1200, 1201,1202, 1203, 1204, 1205, 1206, 1207,1208, 1209, 1210, 1211, 1212℄Wieland, Alexis P., [1213℄Wieland, F., [440℄Wienholt, Willfried, [1214, 1215℄Wilke, Peter, [1216, 1217℄Wilke, P., [243℄Williams, Bryn V., [880℄Williams, G. J., [616℄Williams, Tom, [234℄Williamson, A. G., [303℄Wilson, Stewart W., [1154℄Win eld, A., [396℄Winkler, David A., [530℄Winterer, G., [375℄Wise, B. M., [161℄Won, Kyoung-Jae, [741℄Wong, F., [47℄Wong, I. W., [364℄Woo, Kwang-Bang, [474, 483℄Wood, Dan, [904℄Worden, K., [109, 200, 423℄Wrede, Paul, [50, 122, 138,185, 264℄Wu, C. Y., [516℄Wu, Chen-Phon, [252℄Wu, J. L. C., [584℄Wu, Jean-Lien C., [257℄Wu, J.-L. C., [745℄Wu, Kun Hsiang, [473℄Wu, Wen-Lan, [22℄Wu, Wen-Teng, [712℄Wu, Y., [225℄Xi, Yugeng, [561℄Xia, Zhi-zhong, [837℄Xianbin, Guan, [794℄ Authors 31Xiangwu, Meng, [646℄Xiang-Wu, Meng, [786℄Xiaohui, Zhang, [794℄Xiaoming, Xu, [554℄Xibilia, M. G., [888, 889℄Xie, Weixin, [304℄Xinmin, Huang, [554℄Xiong, Y., [1220℄Xu, Jinwu, [341℄Xu, Wen, [642℄Xuan, Q. Y., [650℄Yabuta, Tetsuro, [1182℄Yamada, Takayuki, [1182℄Yamada, T., [305, 653℄Yamagata, Y., [413℄Yamamoto, H., [189℄Yamamoto, T., [576℄Yamamoto, Y., [790℄Yamany, S. M., [621℄Yamau hi, Toshiyuki, [1099℄Yamazaki, N., [549℄Yan, Wei, [684℄Yanda, Li, [382℄Yanfeng, Cheng, [459℄Yang, Jinn-Moon, [734℄Yang, R. L., [755℄Yang, Xiaowei, [791℄Yang, Zi-Jiang, [687℄Yao, S., [295℄Yao, X. Q., [269℄ Yao, Xin, [323, 326,327, 442, 484, 616, 651, 685, 787,803, 1218, 1219℄Yashioka, M., [762℄Yasuda, Keii hiro, [245℄Yazgan, E., [83℄Yegnanarayana, B., [590, 671℄Yih, Y. W., [269℄Yih, Yuehwern, [220℄Yiming, Zhang, [794℄Yip, P. P. C., [145℄Yip, Per y P. C., [244℄Yokoyama, Ryui hi, [245℄Yon, Jung-Heum, [741℄Yoon, Byungjoo, [110℄Yoon, Seong-Sik, [406℄Yoshimoto, Katsuhisa, [245℄Yoshioka, Mi hifumi, [629, 679℄Yoshizawa, Shuji, [389, 872℄Youkun, Lei, [519℄Yu, Jung-Shik, [474, 483℄Yuanping, Ni, [793℄Zagorski, Peter, [32, 115℄Zalesski, George, [532℄Zamparelli, Mi hele, [656℄Zamzow, Thomas, [560℄Zanela, A., [430℄Ze un, Zhou, [645℄Zeigler, Bernard P., [197, 372, 472℄Zelinka, Ivan, [838℄Zeng, X. Y., [788℄Zhang, Bo, [485℄ Zhang, B.-T., [137℄Zhang, Byoung-Tak, [27, 113, 171,450, 513, 1092, 1093, 1195, 1221℄Zhang, Ching, [117℄Zhang, D., [146℄Zhang, Jianping, [380℄Zhang, Liang-Jie, [319℄Zhang, Liangjie, [381℄Zhang, P. X., [392℄Zhang, Qizhi, [759℄Zhang, Yan-Qing, [500℄Zhang, Z. J., [496℄Zhang, Zhaohui, [485℄Zhang, Zhixiong, [306℄Zhao, Qiangfu, [397, 489,493, 610℄Zheng, Guang L., [166℄Zhenya, He, [444℄Zhitong, Sui, [392℄Zhizheng, Wu, [554℄Zhongjun, Zhang, [478℄Zhou, Chunguang, [791℄Zhou, Yaohe, [759℄Zhou, Yuanhui, [485, 654℄Zhou, Ze un, [642℄Zhu, Zhaoda, [684℄Zitar, Raed Abu, [246℄Zo a, L., [308℄Zuo, Kewei, [712℄total 1228 arti les by 1888 dif-ferent authors 32 Geneti algorithms and neural networks4....

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  • ...1999, [1737, 1738, 1739, 1740, 1741, 1742, 1743, 1744, 1745, 1746, 1747, 1748, 1749, 52, 1750, 1751, 1752, 1753, 53, 1754, 1755, 1756, 1757, 1758, 1759, 1760, 1761, 1762, 1763, 1764, 1765, 1766, 1767, 1768, 1769, 1770, 1771, 1772, 1773, 1774, 1775, 1776, 1777, 1778, 1779, 1780, 1781, 1782, 1783, 1784, 1785, 1786, 1787, 1788, 1789, 1790, 1791, 54, 1792, 1793, 1794, 1795, 1796, 1797, 1798, 64, 1799, 1800, 1801, 1886, 1802, 1803, 1804, 65, 1805, 1806, 1807, 1808, 1809, 1810, 1811, 1812, 1813, 66, 1814, 1815, 1816, 1817, 1818, 1819, 1820, 1821, 1822, 1823, 1824, 1825, 1826, 1827, 1828, 1829, 1830, 1831, 1832, 1833, 1834, 1835, 1836, 1837, 1838, 1839, 1840, 1841, 1842, 1843, 1844, 1845, 55, 1846, 1847, 1848, 1849, 1850, 1851, 1852, 1853, 1854, 1855, 1856, 1857, 1858, 1859] 2000, [484, 485, 486, 487, 488, 489, 490, 491, 492, 493, 56, 494, 495, 496, 497, 498, 499, 500, 501, 502, 503, 504, 505, 14, 506, 507, 508, 509, 510, 511, 512, 513, 514, 515, 516, 517, 1863, 1864, 518, 519, 520, 521, 522, 523, 524, 525, 526, 527, 528, 529, 530, 531, 532, 533, 1865, 534, 535, 536, 537, 538, 539, 1866, 540, 1867, 541, 542, 543, 544, 545, 546, 1868, 547, 548, 549, 550, 551, 552, 553, 554, 555, 556, 557, 558, 559, 560, 561, 562, 563, 564, 1869, 565, 566, 567, 568, 569, 570, 571, 572, 573, 574, 668] 2001, [575, 57, 576, 577, 578, 579, 580, 581, 15, 582, 583, 584, 585, 586, 587, 588, 589, 590, 591, 592, 593, 594, 595, 596, 597, 598, 599, 600, 1870, 601, 602, 16, 603, 604, 605, 606, 607, 608, 17, 609, 610, 611, 612, 613, 614, 615, 616, 617, 618, 619, 620, 621, 622, 623, 624, 625, 626, 627, 628, 629, 630, 631, 632, 633, 634, 18, 635, 636, 637, 638, 639, 1871, 640, 641, 642, 643, 644, 645, 646, 647, 648, 649, 650, 651] 2002, [652, 653, 654, 655, 656, 657, 658, 659, 660, 661, 662, 663, 664, 665, 666, 667, 669, 670, 671, 672, 673, 674, 675, 676, 677, 678, 679, 680, 681, 682, 683, 684, 685, 686, 687, 688, 689, 690, 691, 692, 692, 693, 694, 695, 696, 19, 697, 20, 698, 1872, 699, 700, 701, 702, 703, 1873, 704, 705, 706, 707, 708, 709, 710, 711, 712]...

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  • ...Nakashima, Tomoharu, [588, 1551] Nakayama, Hirotaka, [359] Nakazono, K....

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  • ...Argentina: [261, 339, 349℄ Australia: [1218, 873, 1219, 65, 70, 198, 215, 219, 228,239, 293, 323, 326, 327, 442, 466, 484, 530, 566, 597,616, 644, 651, 685, 702, 780, 787, 803, 815℄ Austria: [178, 192, 462, 624℄ Azerbaithzan: [236℄ Belgium: [1223, 1224, 1226, 1227, 1228, 1229, 1230,1225, 1231, 1233, 1234, 1235, 1174, 1236, 1237, 135,241, 289, 418, 19℄ Brazil: [1222, 156, 168, 240, 287, 307, 332, 506, 525,728, 743, 800, 835℄ Bulgaria: [272, 580, 705℄ Byelorussia: [578℄ Canada: [28, 76, 117, 248, 273, 337, 364, 488, 497, 638,643, 657, 698, 784℄ Chile: [180℄ China (in l. Hong Kong): [420, 381, 69, 266, 291, 295,304, 319, 341, 382, 392, 441, 444, 459, 478, 485, 496,519, 520, 523, 536, 539, 554, 561, 568, 628, 642, 645,646, 647, 648, 654, 684, 759, 786, 791, 793, 794, 822,824, 837, 18, 91, 235, 571, 819℄ Cyprus: [1087, 394℄ Cze h Republi : [214, 324, 613, 641, 676, 708, 720, 838,231, 384, 422, 433℄ Denmark: [52, 277, 297, 311, 320, 416, 833℄ Egypt: [157, 527, 550, 825℄ Finland: [1088, 11, 1089, 78, 131, 160, 190, 267, 333,359, 379, 451, 475, 533, 555, 559, 570, 617, 630, 706,746, 796, 797, 804, 821, 839, 840, 841℄ Fran e: [871, 1022, 917, 965, 966, 967, 968, 1164, 51,97, 124, 139, 143, 213, 351, 385, 458, 467, 504, 511, 515,593, 594, 608, 714, 739, 754, 808℄ Germany (in l. DDR): [964, 1006, 1007, 877, 1008,1091, 876, 1009, 1051, 1059, 1127, 1194, 1221, 875, 878,945, 1057, 1058, 1092, 1093, 1120, 1128, 1214, 1215,1216, 1217, 27, 29, 31, 50, 55, 61, 64, 77, 105, 108, 113,114, 115, 126, 128, 136, 140, 169, 171, 202, 210, 230,243, 251, 259, 262, 298, 313, 353, 361, 375, 383, 399,403, 415, 425, 437, 440, 456, 461, 513, 534, 535, 543,560, 589, 595, 609, 626, 637, 656, 737, 767, 772, 777,795, 816, 828℄ Gree e: [1122, 1123, 586, 757, 758, 769℄ Hungary: [1190, 1191, 408℄ I eland: [510℄ India: [1114, 1153, 40, 123, 142, 148, 226, 265, 356, 373,400, 434, 590, 601, 615, 623, 671, 697℄ Ireland: [918℄ Israel: [1103, 1168, 110, 500℄ Italy: [1084, 1110, 1111, 1064, 1085, 1086, 927, 944,1061, 1062, 1113, 41, 72, 75, 86, 238, 285, 308, 334, 336,342, 346, 390, 430, 517, 522, 531, 640, 703, 750, 770, 13℄ Japan: [1020, 1180, 950, 1034, 1041, 1096, 872, 951,952, 953, 954, 955, 956, 957, 958, 959, 960, 1021, 1097,1098, 1099, 1181, 1182, 1185, 1238, 1239, 1240, 1241,1242, 1243, 1244, 24, 48, 59, 62, 81, 82, 96, 100, 104,111, 121, 129, 134, 150, 152, 159, 189, 233, 245, 247,270, 271, 275, 282, 283, 305, 309, 317, 322, 325, 331,352, 358, 366, 369, 374, 389, 391, 397, 413, 414, 427,428, 429, 443, 448, 455, 465, 487, 489, 493, 378, 507,537, 538, 540, 542, 545, 549, 552, 558, 576, 579, 581,600, 602, 603, 610, 629, 631, 632, 633, 634, 649, 653,658, 662, 664, 666, 667, 673, 674, 679, 680, 681, 687,688, 691, 693, 694, 696, 712, 717, 726, 729, 735, 740,747, 749, 752, 762, 763, 773, 774, 775, 776, 778, 779,788, 789, 790, 799, 801, 805, 826, 832, 834, 836℄ Jordan: [209, 280℄ New Zealand: [572, 596, 751℄ Norway: [288℄ Poland: [1105, 112, 217, 250, 276, 310, 315, 321, 424,577, 582, 604, 669, 707, 722, 732, 802℄ Portugal: [340, 565, 753℄ Republi of South Afri a: [765℄ Romania: [347, 556, 725, 781℄ Russia: [885, 116, 274, 431, 502, 636, 659, 738℄ Singapore: [1145, 1146, 1147, 47, 132, 182, 292, 599,719℄ Slovenia: [912, 42, 177, 260, 286℄ South Korea: [1044, 127, 195, 199, 227, 256, 279, 294,335, 343, 372, 388, 404, 405, 406, 407, 450, 468, 471,472, 474, 483, 591, 612, 618, 627, 713, 716, 723, 724,741, 764, 771, 806, 807, 818℄ Spain: [924, 843, 846, 847, 1116, 1157, 44, 94, 211, 312,345, 518, 524, 529, 563, 564, 585, 592, 670, 766, 813, 20℄ Sweden: [1121, 820℄ Switzerland: [1196, 432, 678, 748, 809℄ Taiwan R.o.C.: [57, 224, 252, 257, 419, 473, 479, 494,514, 544, 584, 619, 695, 710, 711, 731, 734, 745, 814,831, 17, 21, 22℄ Thailand: [588℄ The Netherlands: [879, 1109, 874, 976, 1052, 30, 53,107, 167, 186, 301, 316, 355, 376, 393, 546℄ Turkey: [83, 176, 573, 635, 668℄ Ukraina: [203, 417, 575, 768℄ United Arab Emirates: [66, 246℄ United Kingdom: [886, 913, 928, 1012, 1136, 962, 963,985, 1067, 1107, 1137, 1140, 1141, 986, 987, 988, 989,990, 991, 1079, 1080, 1101, 1142, 880, 992, 993, 994,995, 996, 997, 998, 999, 1000, 1001, 1002, 1003, 1015,1030, 1081, 1082, 1083, 1138, 1143, 36, 37, 43, 49, 68,109, 119, 120, 122, 125, 133, 138, 153, 154, 158, 173,175, 179, 183, 185, 191, 193, 200, 201, 205, 221, 223,225, 242, 255, 264, 268, 269, 278, 303, 314, 338, 348,370, 371, 377, 386, 396, 402, 409, 423, 426, 445, 457,460, 464, 469, 477, 492, 495, 498, 503, 512, 547, 557,569, 574, 583, 587, 650, 655, 660, 663, 675, 677, 682,686, 699, 701, 721, 727, 742, 744, 782, 792, 811, 812,829℄ Geographi al index 43 United States: [977, 1033, 1049, 1198, 1199, 862, 906,907, 961, 978, 979, 1200, 1201, 1202, 1203, 1204, 1205,852, 864, 883, 902, 929, 930, 931, 932, 933, 934, 935,980, 1014, 1032, 1035, 1036, 1037, 1050, 1148, 1149,1154, 1155, 1159, 1172, 1176, 1206, 1207, 857, 904, 936,937, 938, 943, 981, 982, 1042, 1117, 1173, 1208, 1209,1213, 1232, 863, 865, 884, 908, 923, 926, 939, 940, 941,946, 947, 969, 970, 983, 984, 1017, 1039, 1040, 1045,1048, 1072, 1073, 1074, 1133, 1134, 1151, 1160, 1169,1177, 1178, 1210, 1211, 1220, 861, 866, 890, 897, 903,905, 909, 925, 942, 948, 949, 1004, 1005, 1029, 1038,1055, 1075, 1076, 1077, 1078, 1129, 1150, 1184, 1197,1212, 23, 25, 26, 33, 34, 35, 38, 39, 45, 46, 54, 58, 60,67, 71, 73, 74, 79, 80, 84, 85, 87, 88, 89, 90, 92, 93, 95,98, 99, 102, 103, 118, 130, 141, 144, 145, 146, 147, 149,151, 155, 162, 163, 164, 170, 172, 174, 181, 184, 187,188, 194, 196, 197, 204, 206, 207, 212, 216, 220, 229, 232, 237, 244, 249, 253, 254, 258, 263, 281, 290, 296,299, 300, 302, 306, 328, 329, 330, 344, 350, 357, 360,362, 363, 367, 368, 380, 398, 410, 411, 412, 421, 435,436, 438, 439, 447, 449, 452, 453, 454, 463, 470, 476,480, 481, 482, 486, 490, 491, 508, 509, 516, 526, 532,541, 548, 553, 562, 598, 605, 611, 614, 620, 652, 661,665, 683, 690, 692, 700, 704, 709, 715, 730, 736, 755,756, 760, 761, 785, 798, 810, 817, 823, 827, 830, 12, 14,15, 16℄ Unknown ountry: [161, 354, 401, 446, 501, 521, 528,551, 567, 606, 607, 621, 622, 625, 639, 672, 689, 733,783℄ Venezuela: [1139, 505℄ Yugoslavia: [218, 718℄ 44 Geneti algorithms and neural networks Bibliography[1℄ John H. Holland....

    [...]

  • ...• Algeria: [809] • Argentina: [1080, 1163, 1174, 741] • Australia: [482, 101, 483, 868, 872, 1005, 1027, 1031, 1032, 1039, 1055, 1115, 1143, 1146, 1147, 1197, 34, 1286, 1311, 1332, 1391, 1413, 1427, 1469, 1491, 1497, 1526, 1535, 1569, 1585, 46, 1697, 1704, 1730, 1768, 1782, 1792, 1807, 521, 649, 701, 734] • Austria: [988, 1002, 1238, 1307, 1503, 1722, 1774, 15, 714] • Azerbaithzan: [1052] • Bangladesh: [808] • Belgium: [137, 138, 140, 141, 142, 143, 144, 139, 145, 147, 148, 149, 150, 151, 436, 941, 1057, 1108, 1252, 1378, 1764, 500, 501, 572, 586, 718, 781] • Bosnia and Herzegovina: [704, 737, 758] • Brazil: [131, 961, 978, 1056, 1079, 1106, 1154, 1352, 1384, 1883, 1611, 1622, 1625, 1645, 1724, 1766, 1780, 1786, 1830, 527, 669, 711, 722, 725, 767, 804] • Bulgaria: [1091, 1452, 1663] • Byelorussia: [1449] • Canada: [550, 820, 826, 880, 924, 1065, 1092, 1161, 1195, 37, 1336, 38, 1346, 1519, 1523, 1525, 1540, 1589, 44, 1701, 1810, 1831, 485, 577, 676, 696, 774] • Chile: [991, 579, 644] • China: [1254, 1218, 873, 1060, 1063, 1087, 1110, 1878, 1118, 1123, 1138, 1166, 1215, 1234, 1284, 1285, 1289, 1291, 1304, 1325, 1333, 1345, 1373, 1374, 1398, 1400, 1420, 1432, 1433, 1445, 1508, 1524, 1528, 1529, 1530, 1531, 42, 1537, 1568, 1669, 1670, 1703, 1706, 1708, 50, 1710, 1713, 1759, 1778, 1794, 1795, 1834, 1837, 1849, 492, 516, 519, 526, 554, 561, 1869, 584, 596, 639, 642, 647, 682, 702, 706, 721, 733, 739, 764, 775, 788, 789, 796, 805, 806, 807, 515, 903, 1048, 1454, 1606, 1653, 52, 1773, 1789, 1814, 559, 624, 640, 707, 709, 732, 735] • Cyprus: [345, 1231] • Denmark: [853, 1096, 1113, 1130, 1140, 1250, 1825, 59] • Egypt: [962, 1386, 1412, 1715, 1740, 1798] • Finland: [504, 277, 346, 223, 347, 882, 925, 972, 1000, 1085, 1860, 1156, 1186, 1212, 1297, 1320, 1392, 1421, 1426, 1444, 1494, 1510, 1594, 1648, 1716, 1717, 1731, 1791, 1843, 1857, 1859, 560, 562, 677, 710, 724, 747, 748, 750, 1861, 757, 761, 780, 791] • France: [692, 99, 278, 174, 216, 217, 218, 219, 424, 851, 862, 899, 930, 945, 949, 1023, 1177, 1219, 1221, 1303, 1312, 1356, 1365, 1371, 1465, 1466, 1470, 1486, 1604, 1628, 1636, 1665, 1739, 583] • Germany: [222, 260, 261, 105, 262, 350, 80, 104, 11, 263, 307, 316, 387, 457, 103, 106, 197, 314, 315, 351, 352, 378, 388, 477, 478, 479, 480, 818, 827, 829, 831, 852, 856, 863, 867, 881, 909, 912, 919, 920, 921, 933, 935, 1876, 942, 947, 29, 964, 968, 979, 981, 1012, 1019, 1041, 1059, 1068, 1076, 1081, 1114, 1132, 1139, 1180, 1188, 1206, 1217, 1241, 1249, 1264, 1273, 1280, 1283, 1301, 1306, 1321, 1363, 1367, 1372, 1393, 1394, 1410, 1430, 1451, 1467, 1475, 1487, 1505, 1518, 1538, 1587, 1599, 1614, 1634, 1678, 1690, 1695, 1714, 1750, 1761, 1770, 1783, 1800, 65, 1809, 1812, 1844, 1847, 1855, 533, 536, 537, 542, 552, 557, 567, 568, 574, 581, 587, 589, 591, 1870, 604, 606, 619, 629, 645, 650, 651, 652, 655, 698, 708, 717, 730, 744, 759, 800] • Greece: [665, 380, 381, 1459, 1664, 1666, 1686, 1850, 511, 618, 625, 687, 749, 797] • Hungary: [452, 453, 1256, 1772, 799] • Iceland: [1157, 1362, 1779] • India: [372, 431, 839, 929, 950, 955, 1043, 1083, 1183, 1209, 1237, 1277, 1477, 1479, 1489, 1502, 1557, 1584, 47, 1819, 1851, 488, 489, 1868, 549, 660, 689, 19, 20, 728, 67, 779, 803] • Indonesia: [494, 657] • Iran: [743, 754, 784] • Ireland: [802, 168, 772] • Israel: [363, 428, 917, 1350] • Italy: [548, 342, 368, 369, 321, 343, 344, 84, 178, 196, 318, 319, 371, 13, 841, 875, 879, 890, 1054, 1104, 1126, 1152, 1158, 1160, 1165, 1168, 1178, 1213, 1228, 1269, 1369, 1381, 1395, 1521, 1586, 1658, 1685, 1687, 1737, 1749, 1769, 510, 565, 17, 632, 653, 726, 773, 782, 792, 794] • Japan: [703, 272, 442, 202, 273, 288, 296, 355, 100, 152, 153, 154, 155, 156, 157, 158, 1862, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 274, 356, 357, 358, 359, 446, 449, 456, 821, 823, 838, 848, 860, 865, 885, 886, 898, 902, 908, 916, 928, 937, 940, 958, 959, 967, 985, 1004, 1877, 1045, 1049, 1062, 61, 1089, 1090, 1094, 1101, 1102, 1124, 1128, 1136, 1142, 1145, 1153, 1179, 1190, 1192, 1194, 1204, 1205, 1225, 1227, 1229, 1235, 1236, 1247, 1248, 1266, 1267, 1268, 1271, 1272, 1287, 1288, 1293, 1300, 1310, 1324, 1335, 1337, 1338, 1342, 1211, 1354, 1359, 1396, 1397, 1399, 1401, 1402, 1404, 1409, 1415, 1416, 1417, 1418, 1424, 1425, 1447, 1450, 1453, 1472, 1476, 1480, 1481, 1488, 1509, 1512, 1513, 1514, 1515, 1532, 1533, 1541, 1546, 1548, 1550, 1551, 1556, 1558, 1563, 1564, 1565, 1570, 1572, 1575, 1577, 1578, 1579, 1581, 1591, 1601, 1608, 1620, 1629, 1637, 1638, 1639, 1649, 1651, 1654, 1660, 1680, 1681, 1683, 1684, 1689, 1691, 1692, 1693, 1694, 1696, 1707, 1711, 1712, 1719, 1721, 1725, 1728, 1732, 1733, 1735, 1775, 1788, 1797, 1799, 1801, 1803, 1804, 1806, 1821, 1822, 1823, 1828, 1832, 1839, 1848, 1853, 517, 520, 522, 530, 538, 555, 563, 564, 570, 573, 588, 611, 630, 762, 765, 766] • Jordan: [1018, 1099] • Kuwait: [785] • Lebanon: [783] • Malaysia: [1824, 610, 694] • Mexico: [1765, 1846, 1856, 1858, 486, 668, 770, 771] • New Zealand: [1441, 1468, 1633, 1659, 539, 751] • Norway: [1107] • Poland: [814, 365, 918, 1875, 1029, 1067, 1095, 1129, 1134, 1141, 1263, 1357, 1448, 1455, 1482, 1544, 1553, 1595, 1617, 1623, 1673, 1726, 634, 763] • Portugal: [1164, 1439, 1662, 1864, 609, 755] • Puerto Rico: [1813]...

    [...]

References
More filters
Book
01 Sep 1988
TL;DR: In this article, the authors present the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields, including computer programming and mathematics.
Abstract: From the Publisher: This book brings together - in an informal and tutorial fashion - the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields Major concepts are illustrated with running examples, and major algorithms are illustrated by Pascal computer programs No prior knowledge of GAs or genetics is assumed, and only a minimum of computer programming and mathematics background is required

52,797 citations

01 Jan 1989
TL;DR: This book brings together the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields.
Abstract: From the Publisher: This book brings together - in an informal and tutorial fashion - the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields. Major concepts are illustrated with running examples, and major algorithms are illustrated by Pascal computer programs. No prior knowledge of GAs or genetics is assumed, and only a minimum of computer programming and mathematics background is required.

33,034 citations

Journal ArticleDOI
TL;DR: The nearest neighbor decision rule assigns to an unclassified sample point the classification of the nearest of a set of previously classified points, so it may be said that half the classification information in an infinite sample set is contained in the nearest neighbor.
Abstract: The nearest neighbor decision rule assigns to an unclassified sample point the classification of the nearest of a set of previously classified points. This rule is independent of the underlying joint distribution on the sample points and their classifications, and hence the probability of error R of such a rule must be at least as great as the Bayes probability of error R^{\ast} --the minimum probability of error over all decision rules taking underlying probability structure into account. However, in a large sample analysis, we will show in the M -category case that R^{\ast} \leq R \leq R^{\ast}(2 --MR^{\ast}/(M-1)) , where these bounds are the tightest possible, for all suitably smooth underlying distributions. Thus for any number of categories, the probability of error of the nearest neighbor rule is bounded above by twice the Bayes probability of error. In this sense, it may be said that half the classification information in an infinite sample set is contained in the nearest neighbor.

12,243 citations

Journal ArticleDOI
01 Jul 1972
TL;DR: The convergence properties of a nearest neighbor rule that uses an editing procedure to reduce the number of preclassified samples and to improve the performance of the rule are developed.
Abstract: The convergence properties of a nearest neighbor rule that uses an editing procedure to reduce the number of preclassified samples and to improve the performance of the rule are developed. Editing of the preclassified samples using the three-nearest neighbor rule followed by classification using the single-nearest neighbor rule with the remaining preclassified samples appears to produce a decision procedure whose risk approaches the Bayes' risk quite closely in many problems with only a few preclassified samples. The asymptotic risk of the nearest neighbor rules and the nearest neighbor rules using edited preclassified samples is calculated for several problems.

1,774 citations