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A Comparison of Eleven Static Heuristics for Mapping a Class of Independent Tasks onto Heterogeneous Distributed Computing Systems
, 2001
"... this paper is organized as follows. Section 2 defines the computational environment parameters that were varied in the simulations. Descriptions of the 11 mapping heuristics are found in Section 3. Section 4 examines selected results from the simulation study. A list of implementation parameters and ..."
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Cited by 249 (50 self)
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this paper is organized as follows. Section 2 defines the computational environment parameters that were varied in the simulations. Descriptions of the 11 mapping heuristics are found in Section 3. Section 4 examines selected results from the simulation study. A list of implementation parameters and procedures that could be varied for each heuristic is presented in Section 5
An Indexed Bibliography of Genetic Algorithms in Power Engineering
, 1995
"... s: Jan. 1992  Dec. 1994 ffl CTI: Current Technology Index Jan./Feb. 1993  Jan./Feb. 1994 ffl DAI: Dissertation Abstracts International: Vol. 53 No. 1  Vol. 55 No. 4 (1994) ffl EEA: Electrical & Electronics Abstracts: Jan. 1991  Dec. 1994 ffl P: Index to Scientific & Technical Proceed ..."
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Cited by 79 (10 self)
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s: Jan. 1992  Dec. 1994 ffl CTI: Current Technology Index Jan./Feb. 1993  Jan./Feb. 1994 ffl DAI: Dissertation Abstracts International: Vol. 53 No. 1  Vol. 55 No. 4 (1994) ffl EEA: Electrical & Electronics Abstracts: Jan. 1991  Dec. 1994 ffl P: Index to Scientific & Technical Proceedings: Jan. 1986  Feb. 1995 (except Nov. 1994) ffl EI A: The Engineering Index Annual: 1987  1992 ffl EI M: The Engineering Index Monthly: Jan. 1993  Dec. 1994 The following GA researchers have already kindly supplied their complete autobibliographies and/or proofread references to their papers: Dan Adler, Patrick Argos, Jarmo T. Alander, James E. Baker, Wolfgang Banzhaf, Ralf Bruns, I. L. Bukatova, Thomas Back, Yuval Davidor, Dipankar Dasgupta, Marco Dorigo, Bogdan Filipic, Terence C. Fogarty, David B. Fogel, Toshio Fukuda, Hugo de Garis, Robert C. Glen, David E. Goldberg, Martina GorgesSchleuter, Jeffrey Horn, Aristides T. Hatjimihail, Mark J. Jakiela, Richard S. Judson, Akihiko Konaga...
A Comparison Study of Static Mapping Heuristics for a Class of Metatasks on Heterogeneous Computing Systems
, 1999
"... Heterogeneous computing (HC) environments are well suited to meet the computational demands of large, diverse groups of tasks (i.e., a metatask). The problem of mapping (defined as matching and scheduling) these tasks onto the machines of an HC environment has been shown, in general, to be NPcompl ..."
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Cited by 57 (9 self)
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Heterogeneous computing (HC) environments are well suited to meet the computational demands of large, diverse groups of tasks (i.e., a metatask). The problem of mapping (defined as matching and scheduling) these tasks onto the machines of an HC environment has been shown, in general, to be NPcomplete, requiring the development of heuristic techniques. Selecting the best heuristic to use in a given environment, however, remains a difficult problem, because comparisons are often clouded by different underlying assumptions in the original studies of each heuristic. Therefore, a collection of eleven heuristics from the literature has been selected, implemented, and analyzed under one set of common assumptions. The eleven heuristics examined are Opportunistic Load Balancing, UserDirected Assignment, Fast Greedy, Minmin, Maxmin, Greedy, Genetic Algorithm, Simulated Annealing, Genetic Simulated Annealing, Tabu, and A*. This study provides one even basis for comparison and insights into c...
The exploration/exploitation tradeoff in dynamic cellular genetic algorithms
 IEEE Transactions on Evolutionary Computation
, 2005
"... Abstract—This paper studies static and dynamic decentralized versions of the search model known as cellular genetic algorithm (cGA), in which individuals are located in a specific topology and interact only with their neighbors. Making changes in the shape of such topology or in the neighborhood may ..."
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Cited by 34 (7 self)
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Abstract—This paper studies static and dynamic decentralized versions of the search model known as cellular genetic algorithm (cGA), in which individuals are located in a specific topology and interact only with their neighbors. Making changes in the shape of such topology or in the neighborhood may give birth to a high number of algorithmic variants. We perform these changes in a methodological way by tuning the concept of ratio. Since the relationship (ratio) between the topology and the neighborhood shape defines the search selection pressure, we propose to analyze in depth the influence of this ratio on the exploration/exploitation tradeoff. As we will see, it is difficult to decide which ratio is best suited for a given problem. Therefore, we introduce a preprogrammed change of this ratio during the evolution as a possible additional improvement that removes the need of specifying a single ratio. A later refinement will lead us to the first adaptive dynamic kind of cellular models to our knowledge. We conclude that these dynamic cGAs have the most desirable behavior among all the evaluated ones in terms of efficiency and accuracy; we validate our results on a set of seven different problems of considerable complexity in order to better sustain our conclusions. Index Terms—Cellular genetic algorithm (cGA), evolutionary algorithm (EA), dynamic adaptation, neighborhoodtopopulation ratio. I.
A HighPerformance Mapping Algorithm for Heterogeneous Computing Systems
 15th International Parallel and Distributed Processing Symposium (IPDPS'01
"... A mapping algorithm for heterogeneous computing systems is proposed in this paper. This algorithm utilizes a new indicator — the relative cost — to obtain optimal mapping. The existing Minmin algorithm can be well explained under synergy of this new indicator. It is found that the Minmin algorit ..."
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Cited by 16 (0 self)
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A mapping algorithm for heterogeneous computing systems is proposed in this paper. This algorithm utilizes a new indicator — the relative cost — to obtain optimal mapping. The existing Minmin algorithm can be well explained under synergy of this new indicator. It is found that the Minmin algorithm leaves room for improvement because of its haste to reduce completion time by overlooking the impact of load balance. Our new algorithm retains the advantages of the Minmin algorithm and balances the load very well. It demonstrates the ability to generate good mapping in various heterogeneous environments. 1.
M.: Effects of scalefree and smallworld topologies on binary coded selfadaptive
, 2006
"... Abstract. In this paper we investigate the properties of CEAs with populations structured as Watts–Strogatz smallworld graphs and Albert–Barabási scalefree graphs as problem solvers, using several standard discrete optimization problems as a benchmark. The EA variants employed include selfadapta ..."
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Cited by 11 (2 self)
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Abstract. In this paper we investigate the properties of CEAs with populations structured as Watts–Strogatz smallworld graphs and Albert–Barabási scalefree graphs as problem solvers, using several standard discrete optimization problems as a benchmark. The EA variants employed include selfadaptation of mutation rates. Results are compared with the corresponding classical panmictic EA showing that topology together with selfadaptation drastically influences the search. 1
A Scalable Cellular Implementation of Parallel Genetic Programming
 IEEE Transactions on Evolutionary Computation
, 2003
"... A new parallel implementation of genetic programming based on the cellular model is presented and compared with both canonical genetic programming and the island model approach. The method adopts a load balancing policy that avoids the unequal utilization of the processors. Experimental results on b ..."
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Cited by 8 (5 self)
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A new parallel implementation of genetic programming based on the cellular model is presented and compared with both canonical genetic programming and the island model approach. The method adopts a load balancing policy that avoids the unequal utilization of the processors. Experimental results on benchmark problems of different complexity show the superiority of the cellular approach with respect to the canonical sequential implementation and the island model. A theoretical performance analysis reveals the high scalability of the implementation realized and allows to predict the size of the population when the number of processors and their efficiency are fixed.
Parallel Simulated Annealing Algorithms in Global Optimization
 Journal of Global Optimization
, 2001
"... Abstract. Global optimization involves the difficult task of the identification of global extremities of mathematical functions. Such problems are often encountered in practice in various fields, e.g., molecular biology, physics, industrial chemistry. In this work, we develop five different parallel ..."
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Cited by 7 (0 self)
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Abstract. Global optimization involves the difficult task of the identification of global extremities of mathematical functions. Such problems are often encountered in practice in various fields, e.g., molecular biology, physics, industrial chemistry. In this work, we develop five different parallel Simulated Annealing (SA) algorithms and compare them on an extensive test bed used previously for the assessment of various solution approaches in global optimization. The parallel SA algorithms consist of various categories: the asynchronous approach where no information is exchanged among parallel runs and the synchronous approaches where solutions are exchanged using genetic operators, or where solutions are transmitted only occasionally, or where highly coupled synchronization is achieved at every iteration. One of these approaches, which occasionally applies partial information exchanges (controlled in terms of solution quality), provides particularly notable results for functions with vast search spaces of up to 400 dimensions. Previous attempts with other approaches, such as sequential SA, adaptive partitioning algorithms and clustering algorithms, to identify the global optima of these functions have failed without exception. Key words: Global optimization, Parallel simulated annealing 1.
Decentralized cellular evolutionary algorithms
 Handbook of Bioinspired Algorithms and Applications, volume 7 of Chapman and HallCRC Computer and Information Science Series
, 2005
"... In this chapter we study cellular evolutionary algorithms, a kind of decentralized heuristics, and the importance of their induced exploration/exploitation balance on different problems. It is shown that, by choosing synchronous or asynchronous update policies, the selection pressure, and thus the e ..."
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Cited by 6 (3 self)
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In this chapter we study cellular evolutionary algorithms, a kind of decentralized heuristics, and the importance of their induced exploration/exploitation balance on different problems. It is shown that, by choosing synchronous or asynchronous update policies, the selection pressure, and thus the exploration/exploitation tradeoff, can be influenced directly, without using additional ad hoc parameters. The same effect can be obtained by using synchronous algorithms of different neighborhoodtotopology ratio. All the discussed algorithms are applied to a set of benchmark problems. Our conclusions show that the update methods of the asynchronous versions, as well as the ratio of the decentralized algorithm, have a marked influence on the efficiency and accuracy of the resulting algorithm. 1
Fine Tuning the Scheduling of Tasks through a Genetic Algorithm: Application to Posix1003.1b Compliant Systems
 IN « IEE PROCEEDINGS  SOFTWARE », NUBER 1, VOLUME 150, FEB, 2003
, 2003
"... Most of today's commercial RealTime Operating Systems (RTOSs) offer multiple scheduling policies which are applied on a perprocess basis. The best illustrations of this are the Posix1003.1b compliant OSs that provide two realtime scheduling policies, namely sched_fifo and sched_rr, which und ..."
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Cited by 6 (5 self)
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Most of today's commercial RealTime Operating Systems (RTOSs) offer multiple scheduling policies which are applied on a perprocess basis. The best illustrations of this are the Posix1003.1b compliant OSs that provide two realtime scheduling policies, namely sched_fifo and sched_rr, which under some limited hypotheses are respectively the equivalent of Fixed Priority Preemptive (FPP) and RoundRobin (RR). In the field of processor scheduling, schedulability analysis has been extensively studied and the problem of assessing the schedulability of multipolicy systems has been recently addressed in [38]. When FPP and RR are used in conjunction, no optimal priority/policy assignement, such as Audsley's algorithm for FPP [3], is known, a fortiori when other criteria besides feasibility are considered. Because of the size of the solution space, an exhaustive search is not possible; an optimisation technique is required. A schedulability analysis provides valuable help for the application designer but it simply asserts whether a given configuration is feasible or not, in general it does not propose any feasible configurations (1) and, as stated by Gerber and Hong in [23] "it can rarely help to tune the system (2), which is the inevitable next step". To address problems (1) and (2), we propose in this study an approach using a Genetic Algorithm (GA) to best set task priorities and scheduling policies, according to a chosen criterion, on Posix 1003.1b uniprocessor systems. Moreover, it will be shown in this study that the use of RR, in conjunction with FPP, may improve the schedulability as well as the satisfaction of additional applicationdependent criteria.