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A Survey of Parallel Genetic Algorithms
 CALCULATEURS PARALLELES, RESEAUX ET SYSTEMS REPARTIS
, 1998
"... Genetic algorithms (GAs) are powerful search techniques that are used successfully to solve problems in many different disciplines. Parallel GAs are particularly easy to implement and promise substantial gains in performance. As such, there has been extensive research in this field. This survey att ..."
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Cited by 147 (5 self)
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Genetic algorithms (GAs) are powerful search techniques that are used successfully to solve problems in many different disciplines. Parallel GAs are particularly easy to implement and promise substantial gains in performance. As such, there has been extensive research in this field. This survey attempts to collect, organize, and present in a unified way some of the most representative publications on parallel genetic algorithms. To organize the literature, the paper presents a categorization of the techniques used to parallelize GAs, and shows examples of all of them. However, since the majority of the research in this field has concentrated on parallel GAs with multiple populations, the survey focuses on this type of algorithms. Also, the paper describes some of the most significant problems in modeling and designing multipopulation parallel GAs and presents some recent advancements.
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 Parallel Genetic Algorithm for the Set Partitioning Problem
, 1994
"... In this dissertation we report on our efforts to develop a parallel genetic algorithm and apply it to the solution of the set partitioning problema difficult combinatorial optimization problem used by many airlines as a mathematical model for flight crew scheduling. We developed a distributed stea ..."
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Cited by 66 (1 self)
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In this dissertation we report on our efforts to develop a parallel genetic algorithm and apply it to the solution of the set partitioning problema difficult combinatorial optimization problem used by many airlines as a mathematical model for flight crew scheduling. We developed a distributed steadystate genetic algorithm in conjunction with a specialized local search heuristic for solving the set partitioning problem. The genetic algorithm is based on an island model where multiple independent subpopulations each run a steadystate genetic algorithm on their own subpopulation and occasionally fit strings migrate between the subpopulations. Tests on forty realworld set partitioning problems were carried out on up to 128 nodes of an IBM SP1 parallel computer. We found that performance, as measured by the quality of the solution found and the iteration on which it was found, improved as additional subpopulations were added to the computation. With larger numbers of subpopulations the genetic algorithm was regularly able to find the optimal solution to problems having up to a few thousand integer variables. In two cases, highquality integer feasible solutions were found for problems with 36,699 and 43,749 integer variables, respectively. A notable limitation we found was the difficulty solving problems with many constraints.
A Summary of Research on Parallel Genetic Algorithms
, 1995
"... The main goal of this paper is to summarize the previous research on parallel genetic algorithms. We present an extension to previous categorizations of the parallelization techniques used in this field. We will use this categorization to guide us through a review of many of the most important publi ..."
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Cited by 63 (2 self)
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The main goal of this paper is to summarize the previous research on parallel genetic algorithms. We present an extension to previous categorizations of the parallelization techniques used in this field. We will use this categorization to guide us through a review of many of the most important publications. We will build on this survey to try to identify some of the problems that have not been studied systematically yet. 1 Introduction Genetic Algorithms (GAs) are efficient search methods based on principles of natural selection and population genetics. They are being successfully applied to problems in business, engineering and science (Goldberg, 1994). GAs use randomized operators operating over a population of candidate solutions to generate new points in the search space. In the past few years, parallel genetic algorithms (PGAs) have been used to solve difficult problems. Hard problems need a bigger population and this translates directly into higher computational costs. The basic...
Gradual distributed realcoded genetic algorithms
 151 Z. Michalewicz. Genetic Algorithms + Data Structures = Evolution Programs
, 1999
"... Abstract—A major problem in the use of genetic algorithms is premature convergence, a premature stagnation of the search caused by the lack of diversity in the population. One approach for dealing with this problem is the distributed genetic algorithm model. Its basic idea is to keep, in parallel, s ..."
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Cited by 44 (6 self)
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Abstract—A major problem in the use of genetic algorithms is premature convergence, a premature stagnation of the search caused by the lack of diversity in the population. One approach for dealing with this problem is the distributed genetic algorithm model. Its basic idea is to keep, in parallel, several subpopulations that are processed by genetic algorithms, with each one being independent of the others. Furthermore, a migration mechanism produces a chromosome exchange between the subpopulations. Making distinctions between the subpopulations by applying genetic algorithms with different configurations, we obtain the socalled heterogeneous distributed genetic algorithms. These algorithms represent a promising way for introducing a correct exploration/exploitation balance in order to avoid premature convergence and reach approximate final solutions. This paper presents the gradual distributed realcoded genetic algorithms, a type of heterogeneous distributed realcoded genetic algorithms that apply a different crossover operator to each subpopulation. The importance of this operator on the genetic algorithm’s performance allowed us to differentiate between the subpopulations in this fashion. Using crossover operators presented for realcoded genetic algorithms, we implement three instances of gradual distributed realcoded genetic algorithms. Experimental results show that the proposals consistently outperform sequential realcoded genetic algorithms and homogeneous distributed realcoded genetic algorithms, which are equivalent to them and other mechanisms presented in the literature. These proposals offer two important advantages at the same time: better reliability and accuracy. Index Terms—Crossover operator, distributed genetic algorithms, multiresolution, premature convergence, selective pressure. I.
A Genetic Algorithm for Channel Routing in VLSI Circuits
 Evolutionary Computation
, 1994
"... A new genetic algorithm for channel routing in the physical design process of VLSI circuits is presented. The algorithm is based on a problem specific representation The genetic encoding and our genetic operators are described in detail. The performance of the algorithm is tested on different benchm ..."
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Cited by 18 (4 self)
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A new genetic algorithm for channel routing in the physical design process of VLSI circuits is presented. The algorithm is based on a problem specific representation The genetic encoding and our genetic operators are described in detail. The performance of the algorithm is tested on different benchmarks and it is shown that the results obtained using the proposed algorithm are either qualitatively similar to or better than the best published results.
A Parallel Genetic Algorithm for PerformanceDriven VLSI Routing
 IEEE Transactions on Evolutionary Computation
, 1997
"... This paper presents a novel approach to solve the VLSI channel and switchbox routing problems. The approach is based on a parallel genetic algorithm that runs on a distributed network of workstations. The algorithm optimizes both physical constraints (length of nets, number of vias) and crosstalk ..."
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Cited by 18 (1 self)
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This paper presents a novel approach to solve the VLSI channel and switchbox routing problems. The approach is based on a parallel genetic algorithm that runs on a distributed network of workstations. The algorithm optimizes both physical constraints (length of nets, number of vias) and crosstalk (delay due to coupled capacitance). The parallel approach is shown to consistently perform better than a sequential genetic algorithm when applied to these routing problems. An extensive investigation of the parameters of the algorithm yields routing results that are qualitatively better or as good as the best published results. In addition, the algorithm is able to significantly reduce the occurence of crosstalk.
Parallel Strategies for Metaheuristics
"... We present a stateoftheart survey of parallel metaheuristic developments and results, discuss general design and implementation principles that apply to most metaheuristic classes, instantiate these principles for the three metaheuristic classes currently most extensively used  genetic metho ..."
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Cited by 14 (4 self)
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We present a stateoftheart survey of parallel metaheuristic developments and results, discuss general design and implementation principles that apply to most metaheuristic classes, instantiate these principles for the three metaheuristic classes currently most extensively used  genetic methods, simulated annealing, and tabu search, and identify a number of trends and promising research directions.
RPL2: A Language and Parallel Framework for Evolutionary Computing
 PARALLEL PROBLEM SOLVING FROM NATURE III, LNCS 866
, 1994
"... The Reproductive Plan Language 2 (RPL2) is an extensible interpreted language for writing and using evolutionary computing programs. It supports arbitrary genetic representations, all structured population models described in the literature together with further hybrids, and runs on parallel or s ..."
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Cited by 13 (7 self)
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The Reproductive Plan Language 2 (RPL2) is an extensible interpreted language for writing and using evolutionary computing programs. It supports arbitrary genetic representations, all structured population models described in the literature together with further hybrids, and runs on parallel or serial hardware while hiding parallelism from the user. This paper surveys structured population models, explains and motivates the benefits of generic systems such as RPL2 and describes the suite of applications that have used it to date.
A Hardware Architecture For A Parallel Genetic Algorithm For Image Registration
 in Proceedings of IEE Colloquium on Genetic Algorithms in Image Processing and Vision
, 1994
"... this paper the nature of Parallel Genetic Algorithms is described followed by the application of genetic algorithms to vision systems. A description of a hardware architecture for vision systems is detailed along with various modifications to improve the implementation. A simulation is used to produ ..."
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Cited by 11 (1 self)
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this paper the nature of Parallel Genetic Algorithms is described followed by the application of genetic algorithms to vision systems. A description of a hardware architecture for vision systems is detailed along with various modifications to improve the implementation. A simulation is used to produce results that verify the effectiveness of the hardware architecture and finally conclusions and future work are discussed. Parallel Genetic Algorithms Parallel genetic algorithms can be categorised into three types, 'standard', 'coarse grained' and 'fine grained'. Standard GAs can be implemented on a parallel architecture by distributing the evaluation process over a number of processors (Fogarty 1990). Coarse grained genetic algorithms run several populations of genes in parallel. After a number of generations (G) the separate populations export a set of individuals (n) to other neighbouring populations. G generations is termed an 'epoch' or migration period. Normally a single processor will monitor each population and the processor swaps individuals every epoch thus parallelising the problem over the number of populations (processors) within the multiprocessor system (Figure 1). A variety of topologies have been used to define 'neighbours' typically a simple grid or hypercube (Figure 2) is used with each node corresponding to a processor (Tanese 1989, Cohoon 1990, Muhlenbein 1991, Xu 1992). Theoretical studies of a coarse grain GA have been done by Petty & Leuze 1989. Figure 1: Grid Topology Figure 2: Hypercube Topology (4 Dimensional) Finegrained parallel genetic algorithms act on each member of the population in parallel. Consequently each member of the population performs crossover with its immediate neighbours, where the neighbourhood is defined by the topology and s...