H. Esbensen and E. S. Kuh, "Design Space Exploration Using the Genetic Algorithm", Proc. ISCAS, 1996, vol. 4, pp. 500-503. 212  Home/Search   Document Not in Database   Summary   Related Articles   Check

....is ## complete, but neither # nor# complete. That is whenever B, we will be able to state that A is not worse than B. On the other hand, there are cases A for which this conclusion cannot be drawn, although A is actually not worse than B. The same holds for the two indicators proposed by [6] and [1] We will not discuss these in detail and only remark that the following example can be used to show that both indicators in combination with the Boolean function E : I(A) I(B) are not # complete (and # complete) the Pareto optimal front is P = 1, 1) and A = 2) and B = ....

....name reference Boolean function compatibility completeness I HC enclosing hypercube indicator Section 3.2.1 I 2 (A) I 1 (B) I O objective vector indicator Section 3.2. 1 I i (A) I i (B) I H hypervolume indicator [26] I H (A) I H (B) I W average best weight combination [6] I W (A) I W (B) I D distance from reference set [1] I D (A) I D (B) I #1 unary # indicator Section 3.2.2 I #1 (A) I #1 (B) I PF fraction of Pareto optimal front covered [20] I PF (A) I PF (B) I P number of Pareto points contained Section 3.2.2 I P (A) I P (B) I ER ....

Henrik Esbensen and Ernest S. Kuh. Design space exploration using the genetic algorithm. In IEEE International Symposium on Circuits and Systems (ISCAS'96), volume 4, pages 500--503, Piscataway, NJ, 1996. IEEE Press. 19

....each objective, a wide range of values should be covered by the nondominated solutions. In the literature, some attempts can be found to formalize the above definition (or parts of it) by means of quantitative metrics. Performance assessment by means of weighted sum aggregation was introduced by Esbensen and Kuh (1996). Thereby, a set X 0 of decision vectors is evaluated regarding a given linear combination by determining the minimum weighted sum of all corresponding objective vectors of X 0 . Based on this concept, a sample of linear combinations is chosen at random (with respect to a certain probability ....

Esbensen, H. and Kuh, E. S. (1996). Design space exploration using the genetic algorithm. In IEEE International Symposium on Circuits and Systems (ISCAS'96), Volume 4, pages 500--503, IEEE Press, Piscataway, New Jersey.

....D. 233] Dediu, A. Horia, 271] Devarakonda, R. 14] Dhodhi, Muhammad K. 165, 200, 286] Dote, Y. 73] Drechsler, Rolf, 127, 145, 166, 46, 47, 207, 209, 77, 238, 85, 266, 281, 283] Dunlap, Frank, 101] Eklund, P. W. 206] Elias, John G. 126] Engst, Norbert, 322] Esbensen, Henrik, [127, 156, 159, 237, 249, 264, 270, 300] Eshelman, Larry J. 54] Falck, E. 109] Fekadu, Adhanom A. 113] Feldhousen, E. L. 297, 298] Femia, N. 95, 95] Fiorito, N. 183] Frenzel, James F. 110] Frye, Robert C. 222] Furuhashi, Takeshi, 17] Furuya, Tatsumi, 333, 334] Gardner, Julian W. 113] Garis, Hugo de, 333, 334] ....

....[308, 309] Kirsch, Karlheinz, 139, 154] Kita, H. 141] Kling, R. M. 310] Knight, John P. 320] Koakutsu, S. 195, 268] Koza, John R. 59, 75, 79, 87, 101, 103, 104] Krejsa, Jir i, 82] Krieger, R. 272] Krstic, A. 278] Kruiskamp, Wim, 30, 38] Kuga, Shinipei, 28] Kuh, Ernest S. [237, 249, 264] Kurbel, Karl, 169] Kyuma, Kazuo, 290] Lai, Y. T. 332] Lanchares, J. 94] Landrault, C. 208] Lang, K. J. 58] Larcombe, Steven P. 211] Lavenier, Dominique, 214] Lee, K. L. 40] Lee, Michael A. 270] Lee, Terry, 180, 229] Lee, Yuh Sheng, 163] Leenaerts, Domine, 30, 38] ....

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Henrik Esbensen and Ernest S. Kuh. Design space exploration using the genetic algorithm. In Proceedings of the 1996 IEEE International Symposium on Circuits and Systems, pages 500--503, Atlanta, GA, 12.-15. May 1996. IEEE, Piscataway, NJ. yEEA 1078/97 EI M159230/96 ga96cEsbensen.

....David, 469, 1218] Elofsson, Arne, 1000] El Sharkawi, M. A. 992] Elsimary, H. 199] Emery, G. M. 467] Endo, H. 1200] Endoh, Satoshi, 200] Engebretson, C. J. 201] Engelbrecht, A. P. 713] Erdogan, A. 202] Erives, H. 203, 993] Erol, Osman Kaan, 198] Esbensen, Henrik, [994, 1084, 1198] Esbensen, H. 1220, 1269] Escazut, Cathy, 140, 965] Eshelman, Larry J. 204, 361, 1452] Esme, B. 57] Esparcia Alc azar, Anna I. 995] Ewing, Mark S. 996, 1317] Eyada, O. 564] Eyink, K. G. 589] Eyres, D. E. 891] Fadel, Georges M. 1018] Fagarasan, Florin, 255] Faglia, ....

....K. 1385] Kriv y, Ivan, 804] Kroger, Berthold, 1415] Kruger, S. 1265] Krzanowski, R. M. 1075, 1076] Ku, C. S. 114] Kubo, H. 59] Kubota, Naoyuki, 445, 1181, 1217, 1291] Kubota, N. 1309, 1312, 1315] Kueblbeck, C. 771] Kuester, Rebecca L. 1337] Kuh, Ernest S. [994, 1198] Kuhn, Leslie A. 446, 639] Kuiper, H. 310] Kumamura, S. 302] Kumar, A. 916] Kumar, Anup, 344] Kumbla, Kishan K. 1152] Kuncheva, L. 447] Kundu, S. 1128, 1242] Kuniyoshi, Yasuo, 433] Kunugi, M. 570] 24 Genetic algorithms of 1996 (in proceedings) Kuonen, Pierre, 117, ....

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Henrik Esbensen and Ernest S. Kuh. Design space exploration using the genetic algorithm. In Proceedings of the 1996 IEEE International Symposium on Circuits and Systems, pages 500--503, Atlanta, GA, 12.- 15. May 1996. IEEE, Piscataway, NJ. * EEA 1078/97 EI M159230/96 ga96cEsbensen.

....251, 286, 70, 82, 387] Dunham, B. 405] Duponcheele, Georges, 462] Dyczij Edlinger, R. 149] Eastman, Charles M. 45] Edwards, J. A. 52] Eggert, H. 71, 374] Elias, John G. 215] Elmakis, David, 51] Engel, Michael V. 376] Engst, Norbert, 444] Ersoy, Cem, 232] Esbensen, Henrik, [216, 263, 40, 381, 388, 109] Eshelman, Larry J. 184] Estevez, Pablo A. 287] Etter, D. M. 406, 407] Evans, G. 188] Eyvazova, Z. E. 60] Fabbricatore, P. 461] Falco, I. De, 288] Fang, W. Eugene, 246] Finley, Linda, 376] Fiorito, N. 311] Fisher, G. 408] Fisher, M. H. 47] Fleming, Peter, 69] Fogarty, ....

....Frank, 430] Knight, John P. 182] Koakutsu, S. 417, 130, 418] Kobes, E. 143] Kosak, Corey, 18, 424] Koskimaki, Esa, 73] Kottapalli, M. S. 398] Koza, John R. 74, 85] Krishnamoorthy, C. S. 175] Kroo, I. M. 116] Kruiskamp, Wim, 50, 66] Kuga, Shinipei, 290] Kuh, Ernest S. [381, 388] Kumar, A. 382] Kumar, Anup, 67] Kumar, R. R. 303] Kundu, S. 34, 150] Kundu, Sourav, 13] Kurbel, Karl, 293] Kwasnicka, H. 332] Kyuma, Kazuo, 93] Laananen, David H. 12, 106] Lahdelma, Risto, 471] Lai, L. L. 225] Lai, Y. T. 202] Lampinen, Jouni, 480, 481, 489] Langevin, A. ....

[Article contains additional citation context not shown here]

Henrik Esbensen and Ernest S. Kuh. Design space exploration using the genetic algorithm. In Proceedings of the 1996 IEEE International Symposium on Circuits and Systems, pages 500--503, Atlanta, GA, 12.-15. May 1996. IEEE, Piscataway, NJ. y(EI M159230/96) ga96cEsbensen.

....each objective a wide range of values should be covered by the nondominated solutions. In the literature, some attempts can be found to formalize the above de nition (or parts of it) by means of quantitative metrics. Performance assessment by means of weighted sum aggregation was introduced by Esbensen and Kuh (1996). Thereby, a set X 0 of decision vectors is evaluated regarding a given linear combination by determining the minimum weightedsum of all corresponding objective vectors of X 0 . Based on this concept, a sample of linear combinations is chosen at random (with respect to a certain probability ....

Esbensen, H. and E. S. Kuh (1996). Design space exploration using the genetic algorithm. In IEEE International Symposium on Circuits and Systems (ISCAS'96), Volume 4, Piscataway, NJ, pp. 500-503. IEEE.

.... the optimisation process is very sensitive to the assigned weights that could result in poor solutions [7] An alternative to a combined cost function is to explore the solution space and present a range of alternative non dominated solutions (NDS) that are each optimal for a single parameter [8]. The alternative solutions can then be analysed by the designer to select the solution that satisfies the specified requirements. This removes the need to prioritise parameters during the optimisation process and enable the designer to use his expert knowledge in selecting the best solution. ....

H. Esbensen and E.S. Kuh, "Design space exploration using the genetic algorithm," IEEE Int. Symposium On Circuits and Systems, ISCAS 96, Atlanta, USA, 1996, pp. 500-503

....for real time systems [11] Soma et.al. considered the schedule optimization based on fuzzy inference engine [17] These approaches, however, do not take into account the fact that an execution delay of each job can be imprecise. Many research results are available for design space exploration [1,3,4,7,13]. All of these works differ in the techniques used to generate a design solution as well as the solution justification. These works, however, do not consider the impreciseness in the system attributes such as latency constraints and the execution time of a functional unit. Recently, Karkowski and ....

H. Esbensen and E. S. Kuh. Design space exploration using the genetic algorithm. In Proceedings of the 1996 Interational Symposium on Circuits and Systems, pages 500--503, 1996.

....for each objective a wide range of values should be covered by the nondominated solutions. In the literature, some attempts can be found to formalize the above definition (or parts of it) by means of quantitative metrics. Performance assessment by means of weightedsum aggregation was introduced by Esbensen and Kuh (1996). Thereby, a set X 0 of decision vectors is evaluated regarding a given linear combination by determining the minimum weighted sum of all corresponding objective vectors of X 0 . Based on this concept, a sample of linear combinations is chosen at random (with respect to a certain probability ....

Esbensen, H. and E. S. Kuh (1996). Design space exploration using the genetic algorithm.

....allocation and scheduling for synthesizing these systems. By properly integrating the impreciseness into the design process, the synthesis techniques can be effective and a good initial design can be generated. Many research results are available for design exploration in architectural synthesis [1, 3, 4, 6, 9]. All of these works differ in the techniques used to generate a design solution as well as how to justify (evaluate) a solution. These works, however, do not consider the impreciseness of system attributes such as latency and area. In particular, they assume the worst case execution time (area) ....

H. Esbensen and E. S. Kuh. Design space exploration using the genetic algorithm. In Proceedings of the 1996 Interational Symposium on Circuits and Systems, pages 500--503, 1996.

....subset of goals, they are considered equal with respect to these goals, regardless of their specific values in these dimensions. Hence, when goals are satisfied, they are factored out , focusing the search on the remaining, unsatisfactory dimensions. The above definition of OE is introduced in [4] and extends the definition first introduced in [6] by adding the feasibility vector f and the concept of acceptable solutions. Using OE the solutions of a given set 8 can be ranked : r(OE; 8) jffl 2 8jfl OE OEgj is the rank of OE with respect to 8, i.e. the number of solutions in 8 which are ....

....In interactive mode, a single execution was performed for each circuit, defining the time limit as 1 hour, wall clock time, i.e. including the time spent using the interface. The results are shown in Table 2. The set quality values are obtained using the set quality measure introduced in [4], which accounts for the (g; f)values specified. A smaller value means a higher quality. The output sets obtained by Explorer in 1 hour are always significantly better than those obtained by RW in 5 hours. But more interestingly, all of the five sample execu Output set size Set quality Circuit ....

H. Esbensen, E. S. Kuh, "Design Space Exploration Using the Genetic Algorithm," Proc. of the IEEE International Symposium on Circuits and Systems, Vol. IV, pp. 500-503, 1996.

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H. Esbensen and E. S. Kuh, "Design Space Exploration Using the Genetic Algorithm", Proc. ISCAS, 1996, vol. 4, pp. 500-503. 212

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H. Esbensen and E. S. Kuh, "Design Space Exploration Using the Genetic Algorithm", Proc. ISCAS, 1996, vol. 4, pp. 500-503.

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