Compu-search methodologies II: Scheduling using genetic algorithms and artificial neural networks

Alexandre, Frédéric; Cardeira, Carlos; Charpillet, François; Mammeri, Zoubir; Portmann, Marie-Claude

doi:10.1007/978-1-4613-1195-9_10

Cited by 5 publications

(1 citation statement)

References 27 publications

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“…Many permutation COOs have been proposed in the literature (see for example (Alexandre et al, 1996) for a survey). We will restrict here our presentation to Ule ones which are used in the numerical example of the section 6 and other COOs may be found in the quoted paper such as : partially mapped COO or PMX cross-over designed by Goldberg & Lingle (1985) for traveling salesman problems and used by Biegel and Davern (1990) for scheduling problems, cycle COO designed by Oliver et al (1987) for traveling salesman problems, position based COO designed by Syswerda in Davis (1991) for scheduling problems ... To simplify the presentation, we always use the following sequences for parent I and parent 2: Only one offspring is built.…”

Section: Annexmentioning

confidence: 99%

Performance analysis of permutation cross—over genetic operators

Djerid

Portmann

Villon

1996

Journal of Decision Systems

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We are interested here in the utilization of Genetic Algorithms (GA) as approximation methods for combinatorial optimization. They are stochastic methods using genetic operators on a population. For their design, two points must be worked : the general scheme of the algorithm with parameter adjustment and the design of chromosome contents and genetic operators. The first point poses globally no specific problem. The latter one involves difficulties when binary genes inside the chromosomes are replaced by more general information such as permutations. In this case, the performance of the genetic operators must be analyzed with respect to the considered specific problems and their specific criteria. As performance of mutation operators are analogous to those of neighborhood operators used in well known local search methods, we will focus here only on cross-over operator (COO) performances. The aims of this paper are first to design permutation cross-over performance indicators which express the probable trend of associated criterion variations, second to list literature proposed permutation cross-over operators and to propose some new ones with particular properties, third to present a numerical experiment which compares the different cross-over operators using the defined performance indicators. REsUME. Nous nous interessons ici a /'utilisation des Algorithmes Genitiques (AG) comme methodes approchees pour resoudre des problemes d'optimisation combinatoire. Ce sont des methodes stochastiques qui font progresser Ia qua/ire d'une population au moyen d'operateurs genetiques. Pour leur conception, il faut definir le schema general de l'algoritlune avec l'ajustement des parametres associes ainsi que le codage des chromosomes et les operateurs genetiques. La premiere partie n'est pas specifique au probleme considerl La deuxihne partie Revue des systemes de decision. Volume 5-n°1-2/1996 1 pages 157 ~ 177 Downloaded by [New York University] at 04:13 13 April 2015 158 Revue des systemes de decision. Vol. 5-no 1-2/1996 sou/eve des difficultes pour les problemes ou les chromosomes ne contiennent plus un codage binaire, mais un codage plus complexe comme par exemple des permutations. Dans ce cas, La performance des operateurs genetiques doit etre analysee en fonction du probleme a resoudre. Comme La performance des operateurs de mutation est analogue a La performance des operateurs de voisinage utilises dans des methodes bien connues d'amelioration par voisinage local, nous allons naus interesser ici uniquement a La performance des operateurs de croisement. Le but de ce papier est tout d'abord de concevoir des indicateurs de performance des operateurs de croisement qui donnent La tendance de variation probable de criteres associes, ensuite, de lister les operateurs de croisement de permutations presentes dans La litterature et d'en proposer quelques nouveaux presentant des proprietes particulieres, enfin, de presenter une serie d'experiences qui comparent les operateurs de croisement au moyen des indicateurs de performance de...

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Section: Annexmentioning

confidence: 99%