Carlos A. Coello Coello, Michael Rudnick, and Alan D. Christiansen. Using genetic algorithms for optimal design of trusses. In Proceedings of the Sixth International Conference on Tools with Arti cial Intelligence, pages 88-94, New Orleans, LA, U.S.A., November 1994. IEEE Computer Society Press.  Home/Search   Document Not in Database   Summary   Related Articles   Check

....344] Cheng, C. 348] Cheng, Runwei, 281] Cheu, Wen Chin, 224] Chi, H. 33] Chiang, Hsiao Dong, 95] Chikhani, A. Y. 309] Chincarini, A. 458, 461] Chipperfield, Andrew, 69] Cho, K. H. 406] Cho, Sung Jin, 370] Cho, Tsu Won, 308] Christensen, John P. 237] Christiansen, Alan D. [11, 312] Chu, Chee Hung Henry, 102, 398] Chu, K. H. 225] Chun, Jang Sung, 362] Chung, Tae Kyung, 78] Cioppa, A. Della, 288] Clitherow, P. 408] Cluitmans, L. J. M. 322] Coello Coello, Carlos A. 11, 312] Cohoon, James P. 342, 366, 399, 103, 104, 105] Cole, D. G. 315] Conway, Daniel G. ....

....Sung Jin, 370] Cho, Tsu Won, 308] Christensen, John P. 237] Christiansen, Alan D. 11, 312] Chu, Chee Hung Henry, 102, 398] Chu, K. H. 225] Chun, Jang Sung, 362] Chung, Tae Kyung, 78] Cioppa, A. Della, 288] Clitherow, P. 408] Cluitmans, L. J. M. 322] Coello Coello, Carlos A. [11, 312] Cohoon, James P. 342, 366, 399, 103, 104, 105] Cole, D. G. 315] Conway, Daniel G. 213] Coombs, Susan, 402, 403] Coon, Brett W. 214] Cormier, Denis Roger, 282] Corne, Dave, 291] Cornejo Rodriguez, A. 61] 14 Genetic algorithms and CAD Coverstone Carroll, Victoria L. 59] Cowley, ....

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Carlos A. Coello Coello, M. Rudnick, and Alan D. Christiansen. Using genetic algorithms for optimal design of trusses. In Proceedings of the International Conference on Tools with Artificial Intelligence, pages 88--94, New Orlearns, LA, 6.-9.November 1994. IEEE Computer Society Press, Los Alamitos, CA. y(CCA 4750/95) ga94aCoello.

....easy to implement. The reason is that the exact location of the boundary between the feasible and infeasible regions is unknown in many of the problems for which EAs are intended (e.g. in many cases the constraints are not given in algebraic form, but are the outcome generated by a simulator [27]) It is known that the relationship between an infeasible individual and the feasible region of the search space plays a signi cant role in penalizing such an individual [144] However, it is not clear how to exploit this relationship to guide the search in the most desirable direction. There ....

Carlos A. Coello Coello, Michael Rudnick, and Alan D. Christiansen. Using genetic algorithms for optimal design of trusses. In Proceedings of the Sixth International Conference on Tools with Arti cial Intelligence, pages 88-94, New Orleans, LA, U.S.A., November 1994. IEEE Computer Society Press.

.... used an optimality criterion method, Adeli and Kamal [2] used geometric programming, Chao et al. 7] used quadratic programming, Schmit and Miura [45] used both the CONstrained function MINimization (CONMIN) and the NEW Unconstrained Sequential Minimization Technique (NEWSUMT) and Coello et al. [8] used a simple genetic algorithm with binary representation and binary tournament selection (the search space in that case was smaller than in the work reported in the current paper, because each variable was considered discrete whereas we considered them as continuous in the current paper) The ....

....= 482:505592634, with a standard deviation of 6.351394347. The worst solution found was f( x) 493:80920116, which is slightly better than any of the solutions produced by any of the other techniques depicted in Tables 8 and 9. The best solution previously reported was found by Coello et al. [8] using a population size of 300 running during 100 generations. However, to find the best solution reported, 81 runs were performed (varying the crossover and mutation rates) whereas in our case, we performed only 30 runs. To allow a fair comparison, we decided to run our current GA with a ....

[Article contains additional citation context not shown here]

Carlos A. Coello, Michael Rudnick, and Alan D. Christiansen. Using genetic algorithms for optimal design of trusses. In Proceedings of the Sixth International Conference on Tools with Artificial Intelligence, pages 88--94, New Orleans, LA, November 1994. IEEE Computer Society Press.

....system and a GA (with a population of 300 chromosomes running during 100 generations) using binary and floating point representation, with the procedure described before to adjust its parameters. The corresponding results are shown in Table 5 including the best results reported in the literature [9]. The results for Monte Carlo Method 2 are the same as for Method 1, and the results presented for the Min max method are also the basis for computing the best trade off for all the methods in Osyczka s system. As can be seen from these results, the GA provided the best ideal vector, combining the ....

....results in general [10] The mathematical programming techniques did not provide any reasonable results in this example, mainly because of the high non convexity of the search space and the high number of variables involved. It should be noted that the set of results reported by Coello et al. [9] was produced optimizing only the first objective (i.e. the total weight of the truss) in a discrete manner. Assuming continuous variables, the GA engine for single objective optimization was able to find a lighter truss. As we can see in Table 6, the new GA based approach proposed by the ....

Carlos A. Coello, Michael Rudnick, and Alan D. Christiansen. Using genetic algorithms for optimal design of trusses. In Proceedings of the Sixth International Conference on Tools with Artificial Intelligence, pages 88--94, New Orleans, LA, nov 1994. IEEE Computer Society Press.

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