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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 Proceedings: Ja ..."
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Cited by 73 (8 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 Genetic Approach to the Quadratic Assignment Problem
, 1995
"... The Quadratic Assignment Problem (QAP) is a wellknown combinatorial optimization problem with a wide variety of practical applications. Although many heuristics and semienumerative procedures for QAP have been proposed, no dominant algorithm has emerged. In this paper, we describe a Genetic Algori ..."
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Cited by 54 (7 self)
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The Quadratic Assignment Problem (QAP) is a wellknown combinatorial optimization problem with a wide variety of practical applications. Although many heuristics and semienumerative procedures for QAP have been proposed, no dominant algorithm has emerged. In this paper, we describe a Genetic Algorithm (GA) approach to QAP. Genetic algorithms are a class of randomized parallel search heuristics which emulate biological natural selection on a population of feasible solutions. We present computational results which show that this GA approach finds solutions competitive with those of the best previouslyknown heuristics, and argue that genetic algorithms provide a particularly robust method for QAP and its more complex extensions. 5 A Genetic Approach to the Quadratic Assignment Problem David M. Tate and Alice E. Smith Department of Industrial Engineering 1048 Benedum Hall University of Pittsburgh Pittsburgh, PA 15261 4126249837 4126249831 (Fax) 1. Introduction The Quadrat...
Advanced Search Techniques For Circuit Partitioning
 In DIMACS Series in Discrete Mathematics and Theoretical Computer Science
, 1994
"... . Most real world problems especially circuit layout and VLSI design are too complex for any single processing technique to solve in isolation. Stochastic, adaptive and local search approaches have strengths and weaknesses and should be viewed not as competing models but as complimentary ones. This ..."
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Cited by 15 (3 self)
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. Most real world problems especially circuit layout and VLSI design are too complex for any single processing technique to solve in isolation. Stochastic, adaptive and local search approaches have strengths and weaknesses and should be viewed not as competing models but as complimentary ones. This paper describes the application of a combined Tabu Search [1] and Genetic Algorithm heuristic to guide an efficient interchange algorithm to explore and exploit the solution space of a hypergraph partitioning problem. Results obtained indicate, that the generated solutions and running time of this hybrid are superior to results obtained from a combined eigenvector and node interchange method [11]. 1. Introduction In the combinatorial sense, the layout problem is a constrained optimization problem. We are given a description of a circuit (usually called a netlist) which is a description of switching elements and their connecting wires. We seek an assignment of geometric coordinates of the ci...
Task Scheduling For Multiprocessor Systems Using Memetic Algorithms
"... Abstract: In multiprocessor systems, an efficient scheduling of a parallel program onto the processors that minimizes the entire execution time is vital for achieving a high performance. The problem of multiprocessor scheduling can be stated as finding a schedule for a general task graph to be exec ..."
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Cited by 3 (0 self)
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Abstract: In multiprocessor systems, an efficient scheduling of a parallel program onto the processors that minimizes the entire execution time is vital for achieving a high performance. The problem of multiprocessor scheduling can be stated as finding a schedule for a general task graph to be executed on a multiprocessor system so that the schedule length can be minimize. This scheduling problem is known to be NP Hard. In multiprocessor scheduling problem, a given program is to be scheduled in a given multiprocessor system such that the program’s execution time is minimized. The objective is makespan minimization, i.e. we want the last job to complete as early as possible. The tasks scheduling problem is a key factor for a parallel multiprocessor system to gain better performance. A task can be partitioned into a group of subtasks and represented as a DAG ( Directed Acyclic Graph), so the problem can be stated as finding a schedule for a DAG to be executed in a parallel multiprocessor system so that the schedule can e minimized. This helps to reduce processing time and increase processor utilization. Genetic algorithm (GA) is one of the widely used technique for this optimization. But there are some shortcomings which can be reduced by using GA with another optimization technique, such as simulated annealing (SA). This combination of GA and SA is called memetic algorithms. This paper proposes a new algorithm by using this memetic algorithm technique.
Hybrid Algorithm for Multiprocessor Task Scheduling
"... Multiprocessors have become powerful computing means for running realtime applications and their high performance depends greatly on parallel and distributed network environment system. Consequently, several methods have been developed to optimally tackle the multiprocessor task scheduling problem ..."
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Cited by 1 (0 self)
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Multiprocessors have become powerful computing means for running realtime applications and their high performance depends greatly on parallel and distributed network environment system. Consequently, several methods have been developed to optimally tackle the multiprocessor task scheduling problem which is called NPhard problem. To address this issue, this paper presents two approaches, Modified List Scheduling Heuristic (MLSH) and hybrid approach composed of Genetic Algorithm (GA) and MLSH for task scheduling in multiprocessor system. Furthermore, this paper proposes three different representations for the chromosomes of genetic algorithm: task list (TL), processor list (PL) and combination of both (TLPLC). Intensive simulation experiments have been conducted on different random and realworld application graphs such as GaussJordan, LU decomposition, Gaussian elimination and Laplace equation solver problems. Comparisons have been done with the most related algorithms like: list scheduling heuristics algorithm LSHs, Bipartite GA (BGA) [1] and Priority based MultiChromosome (PMC) [2]. The achieved results show that the proposed approaches significantly surpass the other approaches in terms of task execution time (makespan) and processor efficiency.
On Scheduling Imprecise Tasks in RealTime Distributed Systems
"... The Imprecise Computation technique has been proposed as an approach to the construction of realtime systems that are able to provide both guarantee and flexibility. This paper analyzes the use of Imprecise Computation in the scheduling of distributed realtime applications. Initially it is present ..."
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The Imprecise Computation technique has been proposed as an approach to the construction of realtime systems that are able to provide both guarantee and flexibility. This paper analyzes the use of Imprecise Computation in the scheduling of distributed realtime applications. Initially it is presented an approach to the scheduling of distributed imprecise tasks. Then we discuss the main problems associated with that goal and some possible solutions. Keywords: Realtime systems, scheduling, imprecise computation, distributed systems. Correspondence should be sent to: Rmulo Silva Oliveira Instituto de Informtica Univ. Fed. do Rio Grande do Sul Caixa Postal 15064 Porto AlegreRS, 91501970, Brasil romulo@inf.ufrgs.br Phone: +55 (51) 3166828 Fax: +55 (51) 3191576 On Scheduling Imprecise Tasks in RealTime Distributed Systems Rmulo Silva de Oliveira Joni da Silva Fraga II  Univ. Fed. do Rio Grande do Sul LCMIDAS  Univ. Fed. de Santa Catarina Caixa Postal 15064 Caixa Posta...
Accurate Calculation of Deme Sizes for a Parallel Genetic Scheduling Algorithm
"... Abstract. The accuracies of three equations to determine the size of populations for serial and parallel genetic algorithms are evaluated when applied to a parallel genetic algorithm that schedules tasks on a cluster of computers connected via shared bus. This NPcomplete problem is representative o ..."
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Abstract. The accuracies of three equations to determine the size of populations for serial and parallel genetic algorithms are evaluated when applied to a parallel genetic algorithm that schedules tasks on a cluster of computers connected via shared bus. This NPcomplete problem is representative of a variety of optimisation problems for which genetic algorithms (GAs) have been shown to effectively approximate the optimal solution. However, empirical determination of parameters needed by both serial and parallel GAs is timeconsuming, often impractically so in production environments. The ability to predetermine parameter values mathematically eliminates this difficulty. The parameter that exerts the most influence over the solution quality of a parallel genetic algorithm is the population size of the demes. Comparisons here show that the most accurate equation for the scheduling application is CantúPaz ' serial population sizing calculation based on the gambler's ruin model [1]. The study presented below is part of an ongoing analysis of the effectiveness of parallel genetic algorithm parameter value computations based on schema theory. The study demonstrates that the correct deme size can be predetermined quantitatively for the scheduling problem presented here, and suggests that this may also be true for similar optimisation problems. This work is supported by NASA Grant NAG9140. 1
& PhD Forum The MultiProcessor Scheduling Problem in Phylogenetics
"... Abstract—Advances in wetlab sequencing techniques allow for sequencing between 100 genomes up to 1000 full transcriptomes of species whose evolutionary relationships shall be disentangled by means of phylogenetic analyses. Likelihoodbased evolutionary models allow for partitioning such broad phylog ..."
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Abstract—Advances in wetlab sequencing techniques allow for sequencing between 100 genomes up to 1000 full transcriptomes of species whose evolutionary relationships shall be disentangled by means of phylogenetic analyses. Likelihoodbased evolutionary models allow for partitioning such broad phylogenomic datasets, for instance into gene regions, for which likelihood model parameters (except for the tree itself) can be estimated independently. Present day phylogenomic datasets are typically split up into 100010,000 distinct partitions. While the likelihood on such datasets needs to be computed in parallel because of the high memory requirements, it has not yet been assessed how to optimally distribute partitions and/or alignment sites to processors, in particular when the number of cores is significantly smaller than the number of partitions. We find that, by distributing partitions (of varying lengths) monolithically to processors, the induced load distribution problem essentially corresponds to the wellknown multiprocessor scheduling problem. By implementing the simple Longest Processing Time (LPT) heuristics in the PThreads and MPI version of RAxMLLight, we were able to accelerate run times by up to one order of magnitude. Other heuristics for multiprocessor scheduling such as improved MultiFit, improved ZeroOne, or the Three Phase approach did not yield notable performance improvements. Keywordsscheduling; RAxMLLight; phylogenetics; I.