M.J. Shaw R. Sikora. A double-layered learning approach to acquiring rueles for classification: Integrating genetic algorithms with similarity-based learning. ORSA Journal on Computing, 2(6):27-- 36, 1994.  Home/Search   Document Not in Database   Summary   Related Articles   Check

.... structures have ranged from neural network weights and topologies (Gruau Whitley, 1993; Whitley et al. 1990, 1991, 1993; Whitley Schaffer, 1992) to LISP programs (Koza, 1992) to regions of the instance space similar to decision trees induced by a splitting algorithm (Rendell, 1983, 1985; Sikora Shaw, 1994), to expertsystem rules (Montana, 1990) to weights for a game s evaluation function (Rendell, 1990) to weights and orientations for the k nearest neighbor algorithm (Kelly Davis, 1991; Punch et al. 1993) to finite state automata (Fogel et al. 1966) and context free grammars (Wyard, 1991) ....

....applied to classification (Booker, 1982; Goldberg, 1983; Holland Reitman, 1978; Sedbrook et al. 1991; Stadnyk, 1987) Crowding forces newly generated population elements to replace older elements that are similar. Sequential niching (Beasley et al. 1993) is a third approach, implemented by Sikora and Shaw (1994) in their genetic classification system. Sequential niching repeatedly runs a traditional GA, each time making sure that the population searches a new area of the space. Two previous learning systems that utilize implicit niching methods also deserve mention. In both systems (Greene Smith, ....

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Sikora, R., and M. J. Shaw. 1994. A double-layered learning approach to acquiring rules for classification: Integrating genetic algorithms with similarity-based learning. ORSA Journal on Computing 6(2):174187.

.... [423] New Electronics (UK) 705] Nippon Kikai Gakkai Ronbunshu C Hen, 571] Noise Control Engineering Journal, 22] Optical Engineering, 687, 501] Optics Communication, 391] Optics Communications, 649] Optics Letters, 300, 401, 621] Optimization (UK) 917] ORSA Journal on Computing, [40, 128] Pattern Recognit. Image Anal. Russia) 249, 140] Pattern Recognition, 143, 144, 535, 541, 278, 576, 344, 382, 396, 592, 413, 596, 111, 435, 599, 603] Pattern Recognition Letters, 167, 50, 57, 761, 58, 221, 67, 784, 543, 241, 244, 69, 252, 364, 402, 876, 406, 595, 601, 452, 496, 505] Power ....

....Cho, 525] Sepehri, N. 952, 1016] Servet, I. 1079, 1081] Sethares, William A. 639, 1011] Sevenster, A. A. 1113] Shaffer, Ronald E. 677, 107] Shamir, Joseph, 621] Sharman, K. C. 964, 982, 31] Sharman, Ken C. 766, 835, 939] Sharov, A. M. 901] Sharpe, P. 108] Shaw, Michael J. [40] Shen, Sylvia S. 855] Sherrah, Jamie R. 109] Sherrah, J. 86] Sheth, Beerud, 702] Shimojima, K. 1117] Shin, Hyundoo, 966] Shine, J. A. 55] Shinohara, K. 245] Shioyama, T. 243] Shiozawa, M. 768] Shirakawa, H. 321] Shiraki, H. 296] Shmerko, V. 536] Shyu, Ming Suen, ....

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Riyaz Sikora and Michael J. Shaw. A double-layered learning approach to acquiring rules for classification: integrating genetic algorithm with similarity-based learning. ORSA Journal on Computing, 6(2):174--187, Spring 1994. * CCA 63084/94 ga94aSikora.

.... Evolutionary Economics, 125] Journal of Political Economy, 79] Macroeconomic Dynamics, 172] Management Science, 101, 240] Mathematics and Computers in Simulation, 47] Neural Network World, 51, 214] New Scientist, 22] Nuclear Science and Engineering, 232] ORSA Journal on Computing, [30] Parallel Computing (Netherlands) 146] Physica D, 29] Power Syst. Technol. China) 58] Power Systems Research, 25] Proc Inst Mech Eng Part B J Eng Manuf, 69] Proc. CSEE (China) 176] Prod. Plan. Control (UK) 168] Prod. Plann. Control, 115] RAIRO APII JESA J. Eur. Syst. Autom. ....

....S. 142] Sathyanarayana, K. 246] Scandizzo, S. 66] Schi erl, E. 149] Schirru, Roberto, 51, 68, 77] Schmertmann, C. P. 106] Schwehm, Markus, 43, 57] Seese, D. 86] Sekimoto, Hiroshi, 243] Sere, Kaisa, 40, 80, 114] Shan, Feng, 151] Sharpe, Peter K. 221] Shaw, Michael J. [30] Shim, J. C. 156] Shimano, S. 108] Shuheng, Chen, 151] Sikora, Riyaz, 30] Silva, Carlos, 89] Sinriech, D. 107] Sklansky, Jack, 145] Smith, George D. 34, 196] Smith, M. H. 75] Smith, R. E. 18] Smith, Robert E. 185] Song, Y. H. 16, 58, 64, 73, 113, 159, 159] Song, Yong Hua, ....

[Article contains additional citation context not shown here]

Riyaz Sikora and Michael J. Shaw. A double-layered learning approach to acquiring rules for classication: integrating genetic algorithm with similarity-based learning. ORSA Journal on Computing, 6(2):174-187, Spring 1994. * CCA 63084/94 ga94aSikora.

....cost incurred by the standalone GA. Even the simple use of an SBL program to provide the starting population for a GA, rather than a random initial population as is generally used, produces populations which converge more rapidly and concepts which are superior in accuracy in the business domain [12]. The gross structure of the new hybrid system in this study is similar to PLS2, in that an SBL routine resides within a genetic algorithm control structure. 2 Description of Algorithms 2.1 ID3: a Similarity Based Learner A simple version of the decision tree program ID3 [4] is employed in this ....

....of rule sets instead of individual rules. An example Pittsburgh GA learner is given in [13] This study uses the Michigan approach since it is easiest to apply the HLS hybrid operator to an individual rule instead of an an entire rule set. The control structure of GLS is based on the approach in [12]. Figure 1 gives a pictorial representation of the high level controlling algorithm which invokes the genetic algorithm. GLS uses a GA search on the training set to find the fittest rule which covers the most positive events. This rule is added to the classifier rule set and all positive events ....

Sikora, R. and Shaw, M.J. "A Double-Layered Learning Approach to Acquiring Rules for Classification: Integrating Genetic Algorithms with Similarity-Based Learning," in ORSA Journal On Computing, Spring 1994.

....to a single global optimum. However, some domains require the identification of multiple optima in the domain. Examples of such domains include the learning of useful financial decision knowledge from examples, where identifying relevant knowledge and useful intermediate concepts is important (Sikora Shaw, 1994). Another example is in identifying a set of diverse rules that together can be used as the basis for a classifier system (Horn, Goldberg, Deb, 1994) Traditional GAs can successfully identify the best rule (optimum) in the domain, but are unable to maintain rules of secondary importance due to ....

Sikora, R., & Shaw, M. J. (1994). A double-layered learning approach to acquiring rules for class ification: Integrating genetic algorithms with similarity-based learning. ORSA Journal on Computing , 6 (2), 174-- 187.

....to a single global optimum. However, some domains require the identification of multiple optima in the domain. Examples of such domains include the learning of useful financial decision knowledge from examples, where identifying relevant knowledge and useful intermediate concepts is important (Sikora Shaw, 1994). Another example is in identifying a set of diverse rules that together can be used as the basis for a classifier system (Horn, Goldberg, Deb, 1994) Traditional GAs can successfully identify the best rule (optimum) in the domain, but are unable to maintain rules of secondary importance due to ....

Sikora, R., & Shaw, M. J. (1994). A double-layered learning approach to acquiring rules for class ification: Integrating genetic algorithms with similarity-based learning. ORSA Journal on Computing , 6(2), 174--187.

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M.J. Shaw R. Sikora. A double-layered learning approach to acquiring rueles for classification: Integrating genetic algorithms with similarity-based learning. ORSA Journal on Computing, 2(6):27-- 36, 1994.

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