Jim enez, F., Verdegay, J.L.: Evolutionary techniques for constrained optimization problems. In Zimmermann, H.J., ed.: 7th European Congress on Intelligent Techniques and Soft Computing (EUFIT'99), Aachen, Germany, Verlag Mainz (1999) ISBN 3-89653-808-X.  Home/Search   Document Details and Download   Summary   Related Articles   Check

....cost. Also, it is not clear what is the impact of the segment chosen to search on the overall performance of the algorithm. 3. 2 Min Max An approach similar to a min max formulation used in multiobjective optimization [8] combined with tournament selection was proposed by Jim enez and Verdegay [25]. The selection criteria is based on the following rules: Between two feasible individuals, the one with a higher tness wins. A feasible individual wins over on a infeasible individual. Between two infeasible individuals, the one with the lowest amount of constraint violation wins. This ....

Fernando Jimenez and Jose L. Verdegay. Evolutionary techniques for constrained optimization problems. In Hans-Jurgen Zimmermann, editor, 7th European Congress on Intelligent Techniques and Soft Computing (EUFIT '99), Aachen, Germany, 1999. Verlag Mainz. ISBN 3-89653-808-X.

.... same, where the direction d is projected onto the axis of one variable j in the solution 21 space [16] Additionally, a process of eliminating half of the population is applied at regular intervals (only the less tted solutions are replaced by randomly generated points) Jim enez and Verdegay [81] proposed the use of a min max approach [19] to handle constraints. The main idea of this approach is to apply a set of simple rules to decide the selection process: 1. If the two individuals being compared are both feasible, then select based on the minimum value of the objective function. 2. ....

....discards the worst individuals at each generation. Also, the use of line search increases the cost (computationally speaking) of the approach. Finally, it is not clear what is the impact of the segment chosen to search in the overall performance of the algorithm. Jim enez and Verdegay s approach [81] can hardly be said to be using a multiobjective optimization technique since it only ranks infeasible individuals based on constraint violation. A subtle problem with this approach is that the evolutionary process rst concentrates only on the constraint satisfaction problem and therefore it ....

Fernando Jimenez and Jose L. Verdegay. Evolutionary techniques for constrained optimization problems. In 7th European Congress on Intelligent Techniques and Soft Computing (EUFIT'99), Aachen, Germany, 1999. Springer-Verlag.

....the constrained one. 4 IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION, 2000. TEC#311R The traditional constraint handling technique used in evolution strategies falls roughly into the category of over penalization since all infeasible individuals are regarded worse than feasible ones [20] 4] [11]. In fact, canonical evolution strategies (ES) allow only feasible individuals in the initial population. To perform constrained optimization an ES may be used to nd a feasible initial population by minimizing the penalty function ( 20] page 115) Once a feasible population is found, the ES ....

F. Jimenez and J.L. Verdegay. Evolutionary techniques for constrained optimization problems. In Proc. of the 7th European Congress on Intelligent Techniques and Soft Computing (EUFIT'99), Germany, Berlin, 1999. Springer-Verlag.

....the technique discards the worst individuals at each generation. Also, the use of line search increases the cost (computationally speaking) of the approach and it is not clear what is the impact of the segment chosen to search in the overall performance of the algorithm. Jim enez and Verdegay [30] proposed the use of a min max approach [6] to handle constraints. The main idea of this approach is to apply a set of simple rules to decide the selection process: 1. If the two individuals being compared are both feasible, then select based on the minimum value of the objective function. 2. If ....

Fernando Jim'enez and Jos'e L. Verdegay. Evolutionary techniques for constrained optimization problems. In 7th European Congress on Intelligent Techniques and Soft Computing (EUFIT'99), Aachen, Germany, 1999. Springer-Verlag.

.... the same, where the direction d is projected onto the axis of one variable j in the solution space [12] Additionally, a process of eliminating half of the population is applied at regular intervals (only the less fitted solutions are replaced by randomly generated points) Jim enez and Verdegay [67] proposed the use of a min max approach [14] to handle constraints. The main idea of this approach is to apply a set of simple rules to decide the selection process: 1. If the two individuals being compared are both feasible, then select based on the minimum value of the objective function. 2. If ....

....then select based on the maximum constraint violation (max OE j (X) forj = 1; m) The individual with the lowest maximum violation wins. Notice the great similarity between this approach and the technique proposed by Deb [31] that was described in a previous section. Jim enez and Verdegay [67] used a real coded GA with uniform crossover [133] nonuniform mutation [83] and tournament selection. Coello [17] proposed the use of a population based multiobjective optimization technique such as VEGA [119] to handle each of the constraints of a single objective optimization problem as an ....

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Fernando Jim'enez and Jos'e L. Verdegay. Evolutionary techniques for constrained optimization problems. In 7th European Congress on Intelligent Techniques and Soft Computing (EUFIT'99), Aachen, Germany, 1999. Springer-Verlag.

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Jim enez, F., Verdegay, J.L.: Evolutionary techniques for constrained optimization problems. In Zimmermann, H.J., ed.: 7th European Congress on Intelligent Techniques and Soft Computing (EUFIT'99), Aachen, Germany, Verlag Mainz (1999) ISBN 3-89653-808-X.

No context found.

Jim enez, F., Verdegay, J.L.: Evolutionary techniques for constrained optimization problems. In Zimmermann, H.J., ed.: 7th European Congress on Intelligent Techniques and Soft Computing (EUFIT'99), Aachen, Germany, Verlag Mainz (1999) ISBN 3-89653-808-X.

No context found.

Fernando Jimenez and Jose L. Verdegay. Evolutionary techniques for constrained optimization problems. In 7th European Congress on Intelligent Techniques and Soft Computing (EUFIT'99), Aachen, Germany, 1999. Springer-Verlag.

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