Results 1  10
of
15
Static Scheduling Algorithms for Allocating Directed Task Graphs to Multiprocessors
, 1999
"... Devices]: Modes of ComputationParallelism and concurrency General Terms: Algorithms, Design, Performance, Theory Additional Key Words and Phrases: Automatic parallelization, DAG, multiprocessors, parallel processing, software tools, static scheduling, task graphs This research was supported ..."
Abstract

Cited by 312 (4 self)
 Add to MetaCart
Devices]: Modes of ComputationParallelism and concurrency General Terms: Algorithms, Design, Performance, Theory Additional Key Words and Phrases: Automatic parallelization, DAG, multiprocessors, parallel processing, software tools, static scheduling, task graphs This research was supported by the Hong Kong Research Grants Council under contract numbers HKUST 734/96E, HKUST 6076/97E, and HKU 7124/99E. Authors' addresses: Y.K. Kwok, Department of Electrical and Electronic Engineering, The University of Hong Kong, Pokfulam Road, Hong Kong; email: ykwok@eee.hku.hk; I. Ahmad, Department of Computer Science, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong. Permission to make digital / hard copy of part or all of this work for personal or classroom use is granted without fee provided that the copies are not made or distributed for profit or commercial advantage, the copyright notice, the title of the publication, and its date appear, and notice is given that copying is by permission of the ACM, Inc. To copy otherwise, to republish, to post on servers, or to redistribute to lists, requires prior specific permission and / or a fee. 2000 ACM 03600300/99/12000406 $5.00 ACM Computing Surveys, Vol. 31, No. 4, December 1999 1.
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 ..."
Abstract

Cited by 90 (10 self)
 Add to MetaCart
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...
Efficient Scheduling of Arbitrary Task Graphs to Multiprocessors using A Parallel Genetic Algorithm
 JOURNAL OF PARALLEL AND DISTRIBUTED COMPUTING
, 1997
"... Given a parallel program represented by a task graph, the objective of a scheduling algorithm is to minimize the overall execution time of the program by properly assigning the nodes of the graph to the processors. This multiprocessor scheduling problem is NPcomplete even with simplifying assumptio ..."
Abstract

Cited by 41 (5 self)
 Add to MetaCart
Given a parallel program represented by a task graph, the objective of a scheduling algorithm is to minimize the overall execution time of the program by properly assigning the nodes of the graph to the processors. This multiprocessor scheduling problem is NPcomplete even with simplifying assumptions, and becomes more complex under relaxed assumptions such as arbitrary precedence constraints, and arbitrary task execution and communication times. The present literature on this topic is a large repertoire of heuristics that produce good solutions in a reasonable amount of time. These heuristics, however, have restricted applicability in a practical environment because they have a number of fundamental problems including high time complexity, lack of scalability, and no performance guarantee with respect to optimal solutions. Recently, genetic algorithms (GAs) have been widely reckoned as a useful vehicle for obtaining high quality or even optimal solutions for a broad range of combinato...
Static and adaptive distributed data replication using genetic algorithms
, 2004
"... Fast dissemination and access of information in large distributed systems, such as the Internet, has become a norm of our daily life. However, undesired long delays experienced by endusers, especially during the peak hours, continue to be a common problem. Replicating some of the objects at multipl ..."
Abstract

Cited by 32 (11 self)
 Add to MetaCart
Fast dissemination and access of information in large distributed systems, such as the Internet, has become a norm of our daily life. However, undesired long delays experienced by endusers, especially during the peak hours, continue to be a common problem. Replicating some of the objects at multiple sites is one possible solution in decreasing network traffic. The decision of what to replicate where, requires solving a constraint optimization problem which is NPcomplete in general. Such problems are known to stretch the capacity of a Genetic Algorithm (GA) to its limits. Nevertheless, we propose a GA to solve the problem when the read/write demands remain static and experimentally prove the superior solution quality obtained compared to an intuitive greedy method. Unfortunately, the static GA approach involves high running time and may not be useful when read/write demands continuously change, as is the case with breaking news. To tackle such case we propose a hybrid GA that takes as input the current replica distribution and computes a new one using knowledge about the network attributes and the changes occurred. Keeping in view more pragmatic scenarios in today’s distributed information environments, we evaluate these algorithms with respect to the storage capacity constraint of each site as well as variations in the popularity of objects, and also examine the tradeoff between running time and solution quality.
Channel Assignment in Cellular Radio using Genetic Algorithms
 Wireless Personal Communications, (Under Review
, 1996
"... . The channel assignment problem has become increasingly important in mobile telephone communication. Since the usable range of the frequency spectrum is limited, the optimal assignment problem of channels has become increasingly important. Recently Genetic Algorithms (GAs) have been proposed as new ..."
Abstract

Cited by 7 (3 self)
 Add to MetaCart
(Show Context)
. The channel assignment problem has become increasingly important in mobile telephone communication. Since the usable range of the frequency spectrum is limited, the optimal assignment problem of channels has become increasingly important. Recently Genetic Algorithms (GAs) have been proposed as new computational tools for solving optimization problems. GAs are more attractive than other optimization techniques, such as neural networks or simulated annealing, since GAs are generally good at finding an acceptably good global optimal solution to a problem very quickly. In this paper, a new channel assignment algorithm using GAs is proposed. The channel assignment problem is formulated as an energy minimization problem that is implemented by GAs. Appropriate GAs operators such as reproduction, crossover and mutation are developed and tested. In this algorithm, the cell frequency is not fixed before the assignment procedures as in the previously reported channel assignment algorithm usin...
Congressional districting using a TSPbased genetic algorithm
 of Lecture Notes in Computer Science
, 2003
"... Abstract. The drawing of congressional districts by legislative bodies in the United States creates a great deal of controversy each decade as political parties and special interest groups attempt to divide states into districts beneficial to their candidates. The genetic algorithm presented in this ..."
Abstract

Cited by 4 (0 self)
 Add to MetaCart
(Show Context)
Abstract. The drawing of congressional districts by legislative bodies in the United States creates a great deal of controversy each decade as political parties and special interest groups attempt to divide states into districts beneficial to their candidates. The genetic algorithm presented in this paper attempts to find a set of compact and contiguous congressional districts of approximately equal population. This genetic algorithm utilizes a technique based on an encoding and genetic operators used to solve Traveling Salesman Problems (TSP). This encoding forces near equality of district population and uses the fitness function to promote district contiguity and compactness. A postprocessing step further refines district population equality. Results are provided for three states (North Carolina, South Carolina, and Iowa) using 2000 census data. 1 Problem History The United States Congress consists of two houses, the Senate (containing two members from each of the fifty states) and the House of Representatives. The House of Representatives has 435 members, and each state is apportioned a congressional delegation in proportion to its population as determined by a national, decennial census. Each state (usually the state’s legislative body) is responsible for partitioning its state into a number of districts (a districting plan) equal to its apportionment. Through years of case law, the courts have outlined several requirements for the drawing of districts [1]. – The districts must be contiguous. – The districts must be of equal population following the “oneman onevote ” principle.
Data assignment in fault tolerant uploads for digital government applications: a genetic algorithms approach. DG.O
, 2005
"... This paper investigates a data assignment problem in a fault tolerance protocol of Bistro, a wide area upload framework. Uploads correspond to an important class of applications, whose examples include a large number of digital government applications. Specifically, government at all levels is a m ..."
Abstract

Cited by 1 (0 self)
 Add to MetaCart
(Show Context)
This paper investigates a data assignment problem in a fault tolerance protocol of Bistro, a wide area upload framework. Uploads correspond to an important class of applications, whose examples include a large number of digital government applications. Specifically, government at all levels is a major collector and provider of data, and there are clear benefits to disseminating and collecting data over the Internet, given its existing largescale infrastructure and widespread reach in commercial, private, and government domains. In this project we focus on the collection of data over the Internet. By data collection, we mean applications such as Internal Revenue Service (IRS) applications with respect to electronic submission of income tax forms. In Bistro, clients upload their data to intermediaries, known as bistros, to reduce the traffic to the destination around a deadline. The destination server then computes a schedule for pulling the data from bistros after the deadline. In the Bistro framework, bistros can be unavailable or malicious. Thus, a fault tolerance protocol is a vital and fundamental component of the Bistro framework. In this paper, we are particularly interested in a data assignment problem in the Bistro fault tolerance protocol. We formulate this problem into a nonlinear optimization problem and develop a genetic algorithm heuristic as an approximation. We evaluate our approach using simulations and compare the results of our heuristic with other simple heuristics as well as an optimal solution obtained from a bruteforce approach.
Genetic Algorithms Approach to the Channel Assignment Problem
 in Proc. 1995 AsiaPacific Conference on Communications
, 1995
"... The optimal assignment problem of channels has become increasingly important since the usable range of the frequency spectrum is limited. Recently Genetic Algorithms (GAs) have been proposed as new computational tools for solving optimization problems. GAs are more attractive than other optimization ..."
Abstract

Cited by 1 (1 self)
 Add to MetaCart
(Show Context)
The optimal assignment problem of channels has become increasingly important since the usable range of the frequency spectrum is limited. Recently Genetic Algorithms (GAs) have been proposed as new computational tools for solving optimization problems. GAs are more attractive than other optimization techniques, such as neural network or simulated annealing, since GAs are generally good at finding acceptably good global optimal solution to a problem very quickly. In this paper, a new channel assignment algorithm using GAs is proposed. The channel assignment problem is formulated as an energy minimization problem that is implemented by GAs. Appropriate GAs operators such as reproduction, crossover and mutation are developed and tested. In this algorithm, the cell frequency is not fixed before the assignment procedures as in the previously reported channel assignment algorithm using neural networks. All three constraints are also considered for the channel assignments: the cochannel co...
Management Studies
"... The graph partitioning problem has numerous applications in various scientific fields. It usually involves the effective partitioning of a graph into a number of disjoint subgraphs/ zones, and hence becomes a combinatorial optimization problem whose worst case complexity is NPcomplete. The inadequ ..."
Abstract
 Add to MetaCart
The graph partitioning problem has numerous applications in various scientific fields. It usually involves the effective partitioning of a graph into a number of disjoint subgraphs/ zones, and hence becomes a combinatorial optimization problem whose worst case complexity is NPcomplete. The inadequacies of exact methods, like linear and integer programming approaches, to handle largesize instances of the combinatorial problems have motivated heuristic techniques to these problems. In the present work, a multiobjective evolutionary algorithm (MOEA), a kind of heuristic techniques, is developed for partitioning a graph under multiple objectives and constraints. The developed MOEA, which is a modified form of NSGAII, is applied to four randomly generated graphs for partitioning them by optimizing three common objectives under five general constraints. The applications show that the MOEA is successful, in most of the cases, in achieving the expected results by partitioning a graph into a variable number of zones.