C.A.C Coello, Use of self-adaptive penalty approach for engineering optimization problems, Computers in Industry 41, pp. 113-127 (2000).  Home/Search   Document Details and Download   Summary   Related Articles   Check

....the reported experience regarding the mentioned methods. For example, in [8] the following is concluded: Penalties which are functions of the distance from feasibility are better performers than those which are merely functions of the number of violated constraints. Likewise, in [1] 9] [10] and [11] it is assumed that, as seems intuitively satisfying, those methods which take advantage of greater information are the most adequate to establish penalty functions. Interestingly, in none of these the validity of this intuitive dictum is proven either theoretically and or experimentally. ....

Coello, C., "Use of a Self-Adaptive Penalty Approach for Engineering Optimization Problems", Computers in Industry, 41(2):113-127, 2000.

....the references to every journal article included in this bibliography. The list is arranged in alphabetical order by the name of the journal. Archives of Control Sciences, 285] Arti cial Intelligence, 356] Civil Engineering Systems, 132] Complex Systems, 403] Computers in Industry, [139] DIMACS, 196] Engineering Optimization, 136, 138] Engineering with Computers, 89] Evolutionary Computation, 87, 88, 95, 99, 112, 116, 412] Expert Systems with Applications, 114] Fuzzy Systems Arti cial Intelligence Reports and Letters, 176] IEE Proceedings C: Generation, ....

....[315] Chiva, Emmanual, 155] Chorafas, Dimitris N. 12] Christiansen, Alan D. 13, 52, 57, 63, 65, 89, 119, 132, 136] Chu, Chee Hung H. 154] Cli , David T. 156, 159, 161, 192, 367, 368, 370, 371, 372, 373, 374, 376] Clote, Peter, 86] Cockcroft, Victor, 248] Coello Coello, Carlos A. [13, 26, 32, 35, 36, 37, 40, 52, 57, 59, 60, 61, 62, 63, 65, 114, 119, 128, 130, 131, 132, 136, 137, 138, 139] Coello Coello, Carlos C. 89] Collins, Robert James, 398] Colombetti, Marco, 355, 358] Cootes, T. F. 45] Cord on, Oscar, 193, 221, 232, 239, 243, 252, 275, 285, 288, 290, 292] Coveney, Peter V. 125] Crutch eld, James P. 47, 387, 388] Cziko, Gary, 41] Das, Rajarshi, 31, 47] ....

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Carlos A. Coello Coello. Use of a self-adaptive penalty approach for engineering optimization problems. Computers in Industry, 1999. (Accepted for publication; URL: http://www.lania.mx/~ccoello/papers.html) yCoelloCoello Key: ga99eCoelloCoello.

....of the journal. American Journal of Physics, 101] Analytica Chimica Acta, 207, 232] Artificial Intelligence Review, 239] Av. Ing. Quim. 92] Bad. Oper. Decyzje (Poland) 166] Biophysical Journal, 238] Chromatographia, 132] Civil Engineering Systems, 96, 105] Computers in Industry, [109] Egypt. Comput. J. Egypt) 11] Electronics Letters, 197, 20] Engineering Optimization, 106, 108] Engineering with Computers, 94] European Journal of Operational Research, 237] EvoNews, 194] Expert Systems with Applications, 99] Finite Elements in Analysis and Design, 264] Fuzzy ....

....267] Cesteros, A. M. F. P. 183] Chacon, P. 238] Chang, O. 255, 256, 260, 262] Chaves, R. O. 39, 55] Chavez, Margarita G. 268] Cheim, L. 47] Chowdhury, M. M. M. 18] Christiansen, Alan D. 73, 80, 85, 94, 96, 97, 105, 106] Cluitmans, L. J. M. 16] Coello Coello, Carlos A. [71, 73, 74, 75, 76, 77, 78, 80, 81, 82, 83, 84, 85, 87, 94, 96, 97, 99, 102, 103, 104, 105, 106, 107, 108, 109] Colin, A. 92] Colmenares, A. 263] Comellas, F. 245] Conejo, A. J. 184] Conejo, A. 195] Cord on, Oscar, 150, 164, 177, 185, 198, 204, 205, 206, 211, 229, 235] Cornejo Rodriguez, A. 79, 93] Cortez, P. 121] Costa, Ernesto, 130] Costa, J. P. 37] Cotta, Carlos, 186, 209] Cotta ....

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Carlos A. Coello Coello. Use of a self-adaptive penalty approach for engineering optimization problems. Computers in Industry, 1999. (Accepted for publication; available via www URL: http://www.lania.mx/~ccoello/papers.html) yCoelloCoello ga99eCoelloCoello.

....found to provide poor results. However, from the remaining approaches, none of them was found to provide the best possible results with the lowest number of tness function evaluations for all test problems. Obviously, more studies of this sort are required. 2. 5 Co evolutionary penalties Coello [25] proposed the use of a penalty function of the form: tness( x) f( x) coef w 1 viol w 2 ) 36) where f( x) is the value of the objective function for the given set of variable values encoded in a chromosome; w 1 and w 2 are 2 penalty factors (considered as integers) coef is the sum of ....

....into two values (coef and viol) so that the EA has enough information not only about how many constraints were violated, but also about the corresponding amounts of violation. This follows Richardson s suggestion [144] about using penalties that are guided by the distance to feasibility. Coello [25] used two di erent populations P1 and P2 with corresponding sizes M1 and M2. The second of these populations (P 2) encoded the set of weight combinations (w 1 and w 2 ) that would be used to compute the tness value of the individuals in P1 (i.e. P2 contained the penalty factors that would be ....

[Article contains additional citation context not shown here]

Carlos A. Coello Coello. Use of a Self-Adaptive Penalty Approach for Engineering Optimization Problems. Computers in Industry, 41(2):113-127, January 2000.

....string. Deb [15] used binary strings of length 40 in his paper, with which the intrinsic size of the search space is 2 40 1:099 Theta 10 22 . It should be noted that the solution reported here is not the global optimum, and we have found a better solution in previous work (f( x) 1:74830941 [11]) However, that solution was found with over 900; 000 fitness function evaluations per run, whereas in the current paper, we found a solution that is less than 5 below the other one, performing only 5; 000 fitness function evaluations (about 0.6 of the number of evaluations used before) Better ....

Carlos A. Coello Coello. Use of a Self-Adaptive Penalty Approach for Engineering Optimization Problems. Computers in Industry, 41(2):113-- 127, January 2000.

....optimization problems. 4.5 Self Adaptive penalties Michalewicz et al. 86] have recognized the importance of using adaptive penalties in evolutionary optimization, and considered this approach as a very promising direction of research on evolutionary optimization. Following this idea, Coello [19, 16] proposed the use of a penalty function of the form: fitness i (X) f i (X) Gamma (coef Theta w 1 viol Theta w 2 ) 27) where f i (X) is the value of the objective function for the given set of variable values encoded in the chromosome i; w 1 and w 2 are 2 penalty factors (considered as ....

....(coef and viol) so that the GA has enough information not only about how many constraints were violated, but also about the amounts in which such constraints were violated. This follows Richardson s suggestion [115] about using penalties that are guided by the distance to feasibility. Coello [19] used 2 different populations P1 and P2 with corresponding sizes M1 and M2. The second of these populations (P 2) encoded the set of weight combinations (w 1 and w 2 ) that would be used to compute the fitness value of the individuals in P1 (i.e. P2 contained the penalty factors that would be ....

[Article contains additional citation context not shown here]

Carlos A. Coello Coello. Use of a Self-Adaptive Penalty Approach for Engineering Optimization Problems. Computers in Industry, 1999. (Accepted for publication).

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C.A.C Coello, Use of self-adaptive penalty approach for engineering optimization problems, Computers in Industry 41, pp. 113-127 (2000).

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