Runarsson, T.P.; Yao, X.: Stochastic Ranking for Constrained Evolutionary Optimization. IEEE Transactions on Evolutionary Computation, 2000, 4, pp. 284-294. Home/Search Document Details and Download
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IS-PAES: A Constraint-Handling Technique Based on - Multiobjective Optimization.. (Correct)
....We have validated our approach with several problems used as a benchmark for evolutionary algorithms (see [17] and with several engineering optimization problems taken from the standard literature. In the first case, our results are compared against a technique called stochastic ranking [22], which is representative of the state of the art in constrained evolutionary optimization. This approach has been found to be equally good or even better in some cases than the homomorphous maps of Koziel and Michalewicz [15] 5.1 Examples The following parameters were adopted for IS PAES in ....
....The following parameters were adopted for IS PAES in all the experiments reported next: 8 , 5 # # . The maximum number of fitness function evaluations was set to 350,000, which is the number of evaluations used in [22]. We used ten (out of 13) of the test functions described in [22] due to time limitations to perform the experiments. The test functions chosen, however, contain characteristics that are representative of what can be considered difficult global optimization problems for an evolutionary ....
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T.P. Runarsson and X. Yao. Stochastic Ranking for Constrained Evolutionary Optimization. IEEE Transactions on Evolutionary Computation, 4(3):284--294, September 2000.
A Numerical Comparison of some Multiobjective-Based.. - Mezura-Montes, Coello (2002) (Correct)
....) 15. Constraints g 1 , g 2 , g 3 , g 4 , g 5 and g 6 are active. 14 P n (x i ) 2 Q n (x i ) pP n ix (20) subject to: g 1 ( x) 0:75 x i 0 x i 7:5n 0 (21) where n = 20 and 0 x i 10 (i = 1; n) The global maximum is unknown; the best reported solution is [39] f(x ) 0:803619. Constraint g 1 is close to being active (g 1 = 10 ) n subject to: h( x) i 1 = 0 (23) where n = 10 and 0 x i 1 (i = 1; n) The global maximum is at i = 1= n (i = 1; n) where f(x ) 1. 3 0:8356891x 1 x 5 37:293239x 1 ....
....seventeen test functions. Our results provided some insights regarding the behavior of each type of technique. Note however, that comparisons with respect to traditional penalty functions [38, 41] and with the most competitive constraint handling techniques used with EAs (e.g. stochastic ranking [39], the homomourphous maps [27] and the adaptive segregational constrained handling evolutionary algorithm (ASCHEA) 21] are still lacking. The results obtained seem to indicate that techniques based on multiobjective optimization can properly deal with constrained search spaces. However, such ....
Thomas P. Runarsson and Xin Yao. Stochastic Ranking for Constrained Evolutionary Optimization. IEEE Transactions on Evolutionary Computation, 4(3):284-294, September 2000.
Theoretical and Numerical Constraint-Handling Techniques used.. - Coello (2002) (6 citations) (Correct)
....a set of mating restrictions are applied based on the information that each individual has of its own feasibility (this idea was inspired on an earlier approach by Hinterding and Michalewicz [75] so that the global optimum can be reached through cooperative learning. Finally, Runarsson Yao [149] proposed a constraint handling approach based on stochastic ranking that has some resemblance with Surry Radcli e s technique [168] In this case, however, the population is ranked using a stochastic version of bubble sort in which individuals are compared to their adjacent neighbors through a ....
....mechanism of their GA. This makes the approach very ecient (computationally speaking) with respect to other constraint handling techniques, although there are some sacri ces (as in Coello s approach) in terms of quality of the solutions produced. The approach of Runarsson Yao [149] constitutes another promising path of future research in constrainthandling. Their approach is ecient and highly competitive with other (more sophisticated) techniques. Its only current drawback is the need of a parameter (called P f by the authors of the technique) that de nes the probability ....
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Thomas P. Runarsson and Xin Yao. Stochastic Ranking for Constrained Evolutionary Optimization. IEEE Transactions on Evolutionary Computation, 4(3):284-294, September 2000.
On the Usefulness of the Evolution Strategies'.. - Mezura-Montes, Coello (2003) (Correct)
.... or approximate the optimal solution with less fitness function evaluations than other competitive approaches like the Homomorphous Maps of Koziel and Michalewicz [21] Two of the most recent techniques to handle constraints in EAs found in the literature, the Stochastic Ranking by Runarsson Yao [28] and the Adaptive Segregational Contraint Handling Evolutionary Algorithm (ASCHEA) by Hamida Schoenauer [18, 19] are both based on an ES. The quality and consistency of the reported results of both approaches are very good. This suggests that ES s original self adaptation mechanism might help ....
....with constrained search spaces. Thus, we decided to compare three different types of ES ( 71 ) using just three simple comparison criteria to solve the well known benchmark for global nonlinear optimization proposed by Michalewicz and Schoenauer [23] and extended by Runarsson Yao [28]. We also analyze the uselfulness of the correlated mutation in population based ES. This paper is organized as follows: In Section 2 we briefly describe the main concepts of ES. In Section 3, we provide an explanation of the simple constraint handling approach adopted in this work. After that, in ....
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Thomas P. Runarsson and Xin Yao. Stochastic Ranking for Constrained Evolutionary Optimization. IEEE Transactions on Evolutionary Computation, 4(3):284--294, September 2000.
Materialized View Selection as Constrained - Evolutionary Optimization Jeffrey Self-citation (Yao) (Correct)
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T. P. Runarsson and X. Yao, "Stochastic ranking for constrained evolutionary optimization," IEEE Trans. Evol. Comput., vol. 4, pp. 284--294, Sept. 2000.
Reducing Random Fluctuations in - Mutative Self-Adaptation Thomas Self-citation (Runarsson) (Correct)
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T. P. Runarsson and X. Yao. Stochastic ranking for constrained evolutionary optimization. IEEE Transactions on Evolutionary Computation, 4(3):284--294, September 2000.
Unknown - World Congresses Of (Correct)
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Runarsson, T.P.; Yao, X.: Stochastic Ranking for Constrained Evolutionary Optimization. IEEE Transactions on Evolutionary Computation, 2000, 4, pp. 284-294.
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Runarsson, T.P., Yao, X.: Stochastic Ranking for Constrained Evolutionary Optimization. IEEE Transactions on Evolutionary Computation 4 (2000) 284--294
Simple Feasibility Rules and Differential Evolution .. - Mezura-Montes.. (Correct)
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Runarsson, T.P., Yao, X.: Stochastic Ranking for Constrained Evolutionary Optimization. IEEE Transactions on Evolutionary Computation 4 (2000) 284--294
An Improved Diversity Mechanism for Solving Constrained.. - Mezura-Montes, Coello (Correct)
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Runarsson, T.P., Yao, X.: Stochastic Ranking for Constrained Evolutionary Optimization. IEEE Transactions on Evolutionary Computation 4 (2000) 284--294
A Simple Evolution Strategy to Solve - Constrained Optimization Problems (Correct)
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Thomas P. Runarsson and Xin Yao. Stochastic Ranking for Constrained Evolutionary Optimization. IEEE Transactions on Evolutionary Computation, 4(3):284--294, September 2000.
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Th.P. Runarsson & X. Yao, Stochastic Ranking for Constrained Evolutionary Optimization, IEEE Transactions on Evolutionary Computation, vol. 4 (3), pp. 284-294 (2000).
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Runarsson T, Yao X (2000) Stochastic ranking for constrained evolutionary optimization. IEEE Transactions on Evolutionary Computation, 4:284--294.
A Simple Multimembered Evolution Strategy to Solve.. - Mezura-Montes, Coello (2003) (Correct)
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Thomas P. Runarsson and Xin Yao. Stochastic Ranking for Constrained Evolutionary Optimization. IEEE Transactions on Evolutionary Computation, 4(3):284--294, September 2000.
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Thomas P. Runarsson and Xin Yao. Stochastic Ranking for Constrained Evolutionary Optimization. IEEE Transactions on Evolutionary Computation, 4(3):284--294, September 2000.
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Runarsson, T. P. and Yao, X. (2000), Stochastic Ranking for Constrained Evolutionary Optimization. IEEE Transactions on Evolutionary Computation 4(3), 284--294.
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Runarsson T P, Yao X. Stochastic ranking for constrained evolutionary optimization. IEEE Trans. on Evolutionary Computation, 2000, 4(3): 284-294
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Thomas P. Runarsson and Xin Yao. Stochastic Ranking for Constrained Evolutionary Optimization. IEEE Transactions on Evolutionary Computation, 4(3):284--294, September 2000.
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