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 Gorges-Schleuter, Jeffrey Horn, Aristides T. Hatjimihail, Mark J. Jakiela, Richard S. Judson, Akihiko Konaga...

The Quadratic Assignment Problem (QAP) is a well-known combinatorial optimization problem with a wide variety of practical applications. Although many heuristics and semi-enumerative 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 previously-known 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 412-624-9837 412-624-9831 (Fax) 1. Introduction The Quadrat...

. 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...

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.

The Imprecise Computation technique has been proposed as an approach to the construction of real-time systems that are able to provide both guarantee and flexibility. This paper analyzes the use of Imprecise Computation in the scheduling of distributed real-time 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: Real-time 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 Alegre-RS, 91501-970, Brasil romulo@inf.ufrgs.br Phone: +55 (51) 316-6828 Fax: +55 (51) 319-1576 On Scheduling Imprecise Tasks in Real-Time Distributed Systems Rmulo Silva de Oliveira Joni da Silva Fraga II - Univ. Fed. do Rio Grande do Sul LCMI-DAS - Univ. Fed. de Santa Catarina Caixa Postal 15064 Caixa Posta...

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 NP-complete 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 time-consuming, 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 Cantú-Paz ' 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 NAG9-140. 1

Multiprocessors have become powerful computing means for running real-time 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 real-world application graphs such as Gauss-Jordan, 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 Multi-Chromosome (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.