C.A.C. Coello, An Empirical Study of Evolutionary Techniques for Multiobjective Optimization in Engineering Design, Thse de doctorat, Department of Computer Science, Tulane University, Mexique (1996).  Home/Search   Document Not in Database   Summary   Related Articles   Check

.... cannot be accepted either because the combining function used excluded aspects of the problem which were unknown prior to optimization or because we chose an inappropriate setting of the coefficients of the combining function, additional runs may be required until a suitable solution is found [7]. Therefore, we treat the problem of determining an optimal scheduling as a multiobjective problem with noncommensurable objectives. We use the Pareto based optimization, which independently considers multiple objectives, and find a set of solutions that satisfy all of the given objectives. ....

C. A. Coello Coello, An Empirical Study Of Evolutionary Techniques For Multiobjective Optimization In Engineering Design, Ph.D. Thesis, Tulane University, 1996.

....genetic algorithms. For a review of genetic algorithms applied to multiobjective optimization, readers are referred to work by Fonseca and Fleming [23] Literature surveys and comparative studies on multiobjective genetic algorithms are also provided by several other authors, see Coello [11], Horn [37] Tamaki et al. 87] and Zitzler and Thiele [99] In Paper [IV] a discussion of some of the most common algorithms is presented. Here just the multiobjective GA (MOGA) is described, since it is one of the cornerstones of the new multiobjective genetic algorithm being proposed. In the ....

COELLO C., An empirical study of evolutionary techniques for multiobjective optimization in engineering design, Dissertation, Department of Computer Science, Tulane University, 1996.

.... cannot be accepted either because the combining function used excluded aspects of the problem which were unknown prior to optimization or because we chose an inappropriate setting of the coefficients of the combining function, additional runs may be required until a suitable solution is found [7]. Therefore, we treat the problem of determining an optimal scheduling as a multiobjective problem with noncommensurable objectives. We use the Pareto based optimization, which independently considers multiple objectives, and find a set of solutions that satisfy all of the given objectives. ....

C. A. Coello Coello, An Empirical Study Of Evolutionary Techniques For Multiobjective Optimization In Engineering Design, Ph. D. Thesis, Tulane University, 1996.

....generational loop. As determination of Pareto optimality is O(n 2 ) and as the MOGA always returns more nondominated vectors than the NSGA (see Figure 4) its actual execution times are most likely actually much lower (as are the NSGA s) Coello Coello also notes the NSGA s large execution time (Coello, 1996, pp. 187 189) However, even if the MOGA s and NSGA s logic is changed to reflect that of the MOMGA and NPGA (or vice versa) we believe the MOMGA and NPGA are still faster running MOEAs because they are machine executables. 5.2 Additional Experimental Metrics As discussed in Section 3.4, some ....

Coello, C. A. C. (1996). An Empirical Study of Evolutionary Techniques for Multiobjective Optimization in Engineering Design. PhD thesis, Department of Computer Science, Tulane University, New Orleans, LA.

.... evaluated genetic algorithm) in 1985 [279] many procedures based on genetic algorithm principles have been developed to deal with multiple objectives (multiple objective genetic algorithm [88] nondominated sorting GA [296] niched Pareto GA [140] MOGA [221] GA based on a min max strategy [36, 38]) Significant progress in the literature concerns corrections of shortcomings observed in previous algorithms and propositions of new algorithmic primitives to generate a better approximation of E. For example, 111] suggests the use of non domination ranking and selection to move a population ....

....written on MOCO in recent years. Those that we found were not all dedicated to MOCO specifically, but use some MOCO problems in another context: 42] deals with the multiobjective shortest path problem for routing of hazardous material, 195] contains information about bicriteria spanning trees, [36] is about evolutionary techniques in multiobjective optimization, and [72] presents some general results for certain general MOCO problems. Among those which are specifically dedicated to MOCO problems we mention [84] and [184] on the flow problem, 139] and [314] in scheduling. 125] explores the ....

C.A. Coello. An empirical study of evolutionary techniques for multiobjective optimization in engineering design. PhD thesis, Department of Computer Science, Tulane University, New Orleans, LA, 1996.

.... blocks to produce larger circuits) In the past, we have approached this problem using a GA with a matrix encoding scheme, and an n cardinality alphabet (after a series of experiments, we found this n cardinality representation scheme to be more robust than the traditional binary representation (Coello 1996, Coello, Christiansen Aguirre 1997, Coello et al. 2000) Our original GA based approach presents great resemblance with the one proposed by Miller (1997) and further developed by Miller and his colleagues (2000, 1999, 1998) The two main di erences between the two approaches are the encoding ....

....emphasized generation of functional circuits, rather than optimization. It was until recently, that Kalganova Miller (1999) experimented with a two stage (or multiobjective, as they call it) tness function. We adopted that sort of tness function since the beginning of our research in this area (Coello 1996, Coello et al. 1997) However, the use of truly 2 multiobjective optimization techniques (e.g. based on the concept of Pareto optimality (Coello 1999) remained as an open area of research in combinational circuit design, as indicated by Kalganova Miller (1999) In this paper, we propose the ....

Coello, C. A. C. (1996), An Empirical Study of Evolutionary Techniques for Multiobjective Optimization in Engineering Design, PhD thesis, Department of Computer Science, Tulane University, New Orleans, LA.

.... Although it has been argued that a binary representation provides the maximum number of schemata [21] it turns out that in some domains such as numerical optimization, alphabets of higher cardinality have proved to provide better results in a shorter period of time than their binary counterparts [22]. With this idea in mind, we decided to experiment with an alphabet of cardinality n, where n can be defined by the user and will be normally taken as the number of rows allowed in our circuit, according to the matrix encoding adopted in this problem. This representation allows the manipulation of ....

Coello Coello, C. A. (1996) An Empirical Study of Evolutionary Techniques for Multiobjective Optimization in Engineering Design, PhD thesis, Department of Computer Science, Tulane University, New Orleans, Louisiana.

.... objective function is a weighted sum of the pth power of the deviation jf i (x) Gamma T i j [Haimes et al. 1975] Such a formulation has been called generalized goal programming [Ignizio 1976; Ignizio 1981] This technique has also been called target vector optimization by other authors [Coello 1996]. Applications Wienke et al. 1992] used this approach in combination with a genetic algorithm to optimize simultaneously the intensities of six atomic emission lines of trace elements in alumina powder as a function of spectroscopic excitation conditions. Eric Sandgren [1994] also used goal ....

.... will tend to favor more certain objectives when many are present in the problem, because of the randomness involved in the process, and this will have the undesirable consequence of making the population to converge to a particular part of the Pareto front rather than to delineate it completely [Coello 1996]. 5.3 Use of Game Theory We can analyze this technique with reference to a simple optimization problem with two objectives and two design variables whose graphical representation is shown in Figure 9. Let f 1 (x 1 ; x 2 ) and f 2 (x 1 ; x 2 ) represent two scalar objectives and x 1 and An ....

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Coello, C. A. C. 1996. An Empirical Study of Evolutionary Techniques for Multiobjective Optimization in Engineering Design. Ph. D. thesis, Department of Computer Science, Tulane University, New Orleans, LA.

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C.A.C. Coello, An Empirical Study of Evolutionary Techniques for Multiobjective Optimization in Engineering Design, Thse de doctorat, Department of Computer Science, Tulane University, Mexique (1996).

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C. Coello. An Empirical Study of Evolutionary Techniques for Multi--objective Optimization in Engineering Design. PhD thesis, Tulane University, 1996.

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Coello C., An empirical study of evolutionary techniques for multiobjective optimization in engineering design, Dissertation, Department of Computer Science, Tulane University, 1996.

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Coello, C.A.C. (1996) An Empirical Study of Evolutionary Techniques for Multiobjective Optimization in Engineering Design, Doctoral dissertation, Tulane University: New Orleans, LA.

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