P. A. Whigham. Search bias, language bias, and genetic programming. 257 237, Stanford University, CA, USA, 28--31 July 1996. MIT Press.  Home/Search   Document Not in Database   Summary   Related Articles   Check

....a specific return type and distinct argument types) To use strongly typed GP with RAND tree, the user must create a function set with all permutations of both the arity set and return types, else the algorithm will generate invalid tree structures. Other approaches have tried production grammars [20], 7] B ohm and Geyer Schulz [3] extend this approach by selecting trees with exact uniform probability from a tree derivation grammar. Given the absolute maximum bound on tree size S, their approach first compiles (off line) a table #(W, s) of probabilities of producing trees of size s#S derived ....

Whigham, P.A. Search Bias, Language Bias, and Genetic Programming. Genetic Programming 1996: Proceedings of the First Annual Conference (GP96). J. Koza et al, editors. 230--237. Cambridge, MA: The MIT Press, 1996.

....fit schemata) in GP. O Reilly s schema theorem did not include the effects of mutation. In the framework of his GP system based on context free grammars (CFG GP) Peter Whigham produced a very general concept of schema for context free grammars and the related schema theorem [Whigham, 1995, Whigham, 1996b, Whigham, 1996a] In CFG GP programs are the result of applying a set of rewrite rules taken from a pre defined grammar to a starting symbol S. The process of creation of a program can be represented with a derivation tree whose internal nodes are rewrite rules and whose terminals are the ....

Whigham, P. A. (1996b). Search bias, language bias, and genetic programming. In Koza, J. R., Goldberg, D. E., Fogel, D. B., and Riolo, R. L., editors, Genetic Programming 1996: Proceedings of the First Annual Conference, pages 230--237, Stanford University, CA, USA. MIT Press.

....1252, 1282, 1304, 1313] Weishui, Wan, 844] 34 Genetic algorithms of 1996 (in proceedings) Weiss, O. 700] Weistra, J. 157] Wen, Fushuan, 209, 738, 1201] Wen, Guoping, 880] Wenzel, D. 1159] Werten, S. 1419] Wetzlinger, H. 1042] Whidborne, James F. 170, 1191] Whigham, P. A. [845] White, B. A. 797, 1150] White, N. 1055] Whitley, Darrell L. 278, 1468] Whitley, Darrell, 1333, 1469, 1470] Whitley, D. 305] Whitley, L. Darrell, 636] Wieland, Alexis P. 1471] Wierzchon, S. T. 438] Wilby, M. 504] Williams, B. 848] Williams, David J. 974] Williams, ....

.... GACART, 568] GALME, 718] game theory, 190, 1138] iterated prisoner s dilemma, 683] games digit guess, 358] MasterMind, 78] min max, 70] GANNFL, 695] GARTNet, 713] GASP, 1454] gears planetary, 246] generation steady state, 1155] GENESIS, 1402] genetic programming, [1393, 1409, 1410, 1411, 1412, 1413, 1414, 22, 24, 38, 39, 63, 69, 75, 102, 134, 169, 182, 183, 190, 194, 216, 222, 244, 278, 299, 304, 322, 324, 329, 336, 351, 364, 381, 386, 395, 398, 416, 429, 442, 444, 473, 483, 498, 516, 531, 541, 563, 567, 572, 648, 664, 666, 668, 699, 711, 716, 733, 735, 736, 758, 836, 837, 840, 845, 850, 853, 925, 942, 955, 980, 995, 1026, 1030, 1038, 1040, 1045, 1074, 1085, 1100, 1111, 1142, 1158, 1193, 1224, 1235, 1240, 1266, 1267, 1276, 1280, 1297, 1301] genetic programming agents, 626] AI, 661] analysis, 647] C , 1028] classification, 273] code duplication, 786] compact solutions, 90] control rules, 149] controllers fuzzy, 29] crossover, 247] data bases, 745] data mining, 639] data structures, 459] decision ....

P. A. Whigham. Search bias, language bias, and genetic programming. In Koza et al. [1492], page ? yconf.prog ga96aWhigham.

....of the genetic algorithm to the variable sized Turing complete program trees (Teller 1994) and even program graphs (Teller 1996) of genetic programming further compounds the difficulty of the theoretical issues involved. There is increasing work on the grammatical structure of genetic programming (Whigham 1996) and the theoretical basis for genetic programming (Poli and Langdon 1997) 13. Optimization Recent examples of applications of genetic programming to problems of optimization include work (Soule, Foster, and Dickinson 1996) from the University of Idaho, the site of much early work on genetic ....

Whigham, Peter A. Search bias, language bias, and genetic programming. In Koza, John R., Goldberg, David E., Fogel, David B., and Riolo, Rick L. (editors). 1996. Genetic Programming 1996: Proceedings of the First Annual Conference, July 28-31, 1996, Stanford University. Cambridge, MA: MIT Press.

....Vesecky, John F. 235, 268, 275, 368] Vuthichai, A. 229] Wainwright, Roger L. 199, 250, 284, 292] Walsh, Paul, 343, 441, 472] Wang, Jue, 419] Ward, David, 451] Warren, Mark A. 137] Wasiewicz, Piotr, 452, 468] Wasiewicz, P. 423] Watson, Andrew H. 344, 367, 453] Whigham, P. A. [138, 230, 345] Whitley, Darrell L. 288] Whitley, Darrell, 497, 498] Willeke, Thomas, 231] Willis, M. J. 338] Authors 17 Willis, Mark J. 210, 239, 371] Willis, Mark, 316, 413, 454] Wineberg, Mark, 346] Winkeler, Jay F. 455] Wong, M. L. 232] Wong, Man Leung, 110, 209, 238, 246, 252, 258, ....

.... programming, 104] fitting Mackey Glass, 119] formal languages context free, 248] fractals IFS, 187] ftiness limited error, 460] function approximation, 180] GA P, 200] game theory, 279] games, 390] Nim, 422] poker, 392] Tetris, 234] tile puzzle, 188] genetic programming, [489, 485, 490, 506, 483, 487, 488, 37, 507, 38, 39, 41, 42, 43, 44, 45, 46, 47, 48, 49, 474, 475, 501, 508, 40, 50, 51, 52, 53, 54, 55, 56, 57, 58, 525, 530, 536, 537, 77, 493, 494, 499, 500, 509, 511, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 519, 520, 522, 524, 526, 527, 538, 539, 476, 477, 478, 479, 480, 481, 482, 484, 486, 495, 496, 497, 498, 502, 503, 504, 505, 510, 512, 514, 71, 72, 73, 74, 75, 515, 516, 517, 518, 521, 523, 528, 529, 531, 532, 533, 534, 540, 541, 79, 82, 84, 86, 87, 88, 89, 92, 95, 96, 97, 99, 102, 107, 108, 109, 112, 114, 115, 117, 122, 123, 124, 125, 127, 129, 130, 133, 134, 135, 137, 138, 139, 140, 142, 143, 147, 149, 153, 155, 156, 157, 158, 160, 164, 167, 168, 170, 171, 10, 173, 11, 12, 13, 16, 17, 18, 19, 20, 175, 176, 177, 178, 180, 181, 182, 183, 184, 185, 187, 188, 189, 190, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 205, 21, 206, 207, 210, 211, 213, 214, 215, 216, 217, 218, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 233, 234, 236, 237, 22, 238, 239, 240, 241, 242, 244, 245, 246, 248, 249, 250, 252, 253, 254, 255, 257, 258, 259, 260, 307, 261, 262, 26, 264, 265, 266, 267, 270, 272, 275, 277, 278, 279, 280, 281, 283, 284, 288, 289, 290, 292, 293, 294, 296, 298, 301, 302, 303, 304, 305, 306, 308, 309, 310, 27, 313, 314, 315, 316, 317, 318, 320, 321, 322, 323, 328, 330, 331, 332, 333, 334, 335, 336, 337, 338, 339, 341, 343, 344, 345, 346, 347, 348, 28, 349, 350, 351, 352, 355, 357, 358, 359, 360, 29, 361, 362, 363, 364, 366, 367, 368, 371, 372, 373, 374, 375, 32, 376, 377, 378, 379, 380, 381, 382, 383, 384, 385, 386, 387, 388, 390, 391, 392, 393, 394, 395, 396, 397, 400, 402, 403, 404, 405, 407, 408, 409, 410, 411, 412, 413, 414, 415, 417, 421, 422, 423, 33, 425, 426, 427, 429, 430, 432, 433, 435, 437, 438, 439, 440, 442, 443, 444, 445, 446, 447, 448, 449, 450, 451, 453, 454, 455, 456, 459, 461, 34, 462, 463, 464, 465, 466, 467, 468, 35, 469, 470, 471, 36, 472] genetic programming acyclic graphs, 151] agents, 325] AI, 329] analysis, 159, 327] Boolean functions, 398, 460] breeding, 80, 169, 172] C, 434] C , 103, 128, 186, 354, 356] classification, 287] code reuse, 418] combinatorial logic, 428] commercial applications, 243] compact ....

[Article contains additional citation context not shown here]

P. A. Whigham. Search bias, language bias, and genetic programming. In Koza et al. [566], page ? y(conf.prog) ga96aWhigham.

No context found.

P. A. Whigham. Search bias, language bias, and genetic programming. In J. R. Koza, D. E. Goldberg, D. B. Fogel, and R. L. Riolo, editors, Genetic Programming 1996: Proceedings of the First Annual Conference, pages 230--237, Stanford University, CA, USA, 28--31 July 1996. MIT Press. 14

....relatively fit schemata) in GP. O Reilly s schema theorem did not include the effects of mutation. 2. 5 Whigham s GP schemata In the framework of his GP system based on context free grammars (CFG GP) Whigham produced a concept of schema for context free grammars and the related schema theorem [28, 30, 29]. In CFG GP programs are the result of applying a set of rewrite rules taken from a pre defined grammar to a starting symbol S. The process of creation of a program can be represented with a derivation tree whose internal nodes are 7 rewrite rules and whose terminals are the functions and ....

Peter A. Whigham. Search bias, language bias, and genetic programming. In John R. Koza, David E. Goldberg, David B. Fogel, and Rick L. Riolo, editors, Genetic Programming 1996: Proceedings of the First Annual Conference, pages 230--237, Stanford University, CA, USA, 28--31 July 1996. MIT Press.

....blocks (short, low order relatively fit schemata) in GP. O Reilly s schema theorem did not include the effects of mutation. In the framework of his GP system based on context free grammars (CFGGP) Whigham produced a concept of schema for context free grammars and the related schema theorem [18, 20, 19]. In CFG GP programs are the result of applying a set of rewrite rules taken from a pre defined grammar to a start 3 We use here the standard notation for multisets, which is slightly different from the one used in O Reilly s work. ing symbol S. The process of creation of a program can be ....

P. A. Whigham. Search bias, language bias, and genetic programming. In J. R. Koza, D. E. Goldberg, D. B. Fogel, and R. L. Riolo, editors, Genetic Programming 1996: Proceedings of the First Annual Conference, pages 230--237, Stanford University, CA, USA, 28--31 July 1996. MIT Press.

....In the framework of his GP system based on context free grammars (CFG GP) Whigham produced a concept of schema for context free grammars and the related schema theo 2 We use here the standard notation for multisets, which is slightly different from the one used in O Reilly s work. rem [20, 21, 22]. In CFG GP programs are the result of applying a set of rewrite rules taken from a pre defined grammar to a starting symbol S. The process of creation of a program can be represented with a derivation tree whose internal nodes are rewrite rules and whose terminals are the functions and terminals ....

P. A. Whigham. Search bias, language bias, and genetic programming. In John R. Koza, David E. Goldberg, David B. Fogel, and Rick L. Riolo, editors, Genetic Programming 1996: Proceedings of the First Annual Conference, pages 230--237, Stanford University, CA, USA, 28--31 July 1996. MIT Press.

....blocks (short, low order relatively fit schemata) in GP. O Reilly s schema theorem did not include the effects of mutation. In the framework of his GP system based on context free grammars (CFG GP) Whigham produced a concept of schema for context free grammars and the related schema theorem [15, 16, 17]. In CFG GP programs are the result of applying a set of rewrite rules taken from a pre defined grammar to a starting symbol S. The process of creation of a program can be represented with a derivation tree whose internal nodes are rewrite rules and whose terminals are the functions and terminals ....

P. A. Whigham. Search bias, language bias, and genetic programming. In John R. Koza, David E. Goldberg, David B. Fogel, and Rick L. Riolo, editors, Genetic Programming 1996: Proceedings of the First Annual Conference, pages 230--237, Stanford University, CA, USA, 28--31 July 1996. MIT Press.

No context found.

P. A. Whigham. Search bias, language bias, and genetic programming. 257 237, Stanford University, CA, USA, 28--31 July 1996. MIT Press.

No context found.

P. A. Whigham. Search bias, language bias, and genetic programming. In John R. Koza, David E. Goldberg, David B. Fogel, and Rick L. Riolo, editors, Genetic Programming 1996 : Proceedings of the First Conference, Stanford University, CA, USA, 28-- 31 July 1996. MIT Press.

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