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Designing Efficient And Accurate Parallel Genetic Algorithms
, 1999
"... Parallel implementations of genetic algorithms (GAs) are common, and, in most cases, they succeed to reduce the time required to find acceptable solutions. However, the effect of the parameters of parallel GAs on the quality of their search and on their efficiency are not well understood. This insuf ..."
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Cited by 293 (5 self)
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Parallel implementations of genetic algorithms (GAs) are common, and, in most cases, they succeed to reduce the time required to find acceptable solutions. However, the effect of the parameters of parallel GAs on the quality of their search and on their efficiency are not well understood. This insufficient knowledge limits our ability to design fast and accurate parallel GAs that reach the desired solutions in the shortest time possible. The goal of this dissertation is to advance the understanding of parallel GAs and to provide rational guidelines for their design. The research reported here considered three major types of parallel GAs: simple masterslave algorithms with one population, more sophisticated algorithms with multiple populations, and a hierarchical combination of the first two types. The investigation formulated simple models that predict accurately the quality of the solutions with different parameter settings. The quality predictors were transformed into populationsizing equations, which in turn were used to estimate the execution time of the algorithms.
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 172 (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.
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 75 (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...
Memetic Algorithms for Combinatorial Optimization Problems: Fitness Landscapes and Effective Search Strategies
, 2001
"... ..."
Efficient Parallel Genetic Algorithms: Theory and Practice
 Computer Methods in Applied Mechanics and Engineering
, 2000
"... Parallel genetic algorithms (GAs) are complex programs that are controlled by many parameters, which affect their search quality and their efficiency. The goal of this paper is to provide guidelines to choose those parameters rationally. The investigation centers on the sizing of populations, becaus ..."
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Cited by 47 (1 self)
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Parallel genetic algorithms (GAs) are complex programs that are controlled by many parameters, which affect their search quality and their efficiency. The goal of this paper is to provide guidelines to choose those parameters rationally. The investigation centers on the sizing of populations, because previous studies show that there is a crucial relation between solution quality and population size. As a first step, the paper shows how to size a simple GA to reach a solution of a desired quality. The simple GA is then parallelized, and its execution time is optimized. The rest of the paper deals with parallel GAs with multiple populations. Two bounding cases of the migration rate and topology are analyzed, and the case that yields good speedups is optimized. Later, the models are specialized to consider sparse topologies and migration rates that are more likely to be used by practitioners. The paper also presents the additional advantages of combining multi and singlepopulation parallel GAs. The results of this work are simple models that practitioners may use to design efficient and competent parallel GAs.
Memetic Algorithms for the Traveling Salesman Problem
 Complex Systems
, 1997
"... this paper, the tness landscapes of several instances of the traveling salesman problem (TSP) are investigated to illustrate why MAs are wellsuited for nding nearoptimum tours for the TSP. It is shown that recombination{based MAs can exploit the correlation structure of the landscape. A comparis ..."
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Cited by 37 (8 self)
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this paper, the tness landscapes of several instances of the traveling salesman problem (TSP) are investigated to illustrate why MAs are wellsuited for nding nearoptimum tours for the TSP. It is shown that recombination{based MAs can exploit the correlation structure of the landscape. A comparison of several recombination operators { including a new generic recombination operator { reveals that when using the sophisticated Lin{Kernighan local search, the performance dierence of the MAs is small. However, the most important property of eective recombination operators is shown to be respectfulness. In experiments it is shown that our MAs with generic recombination are among the best evolutionary algorithms for the TSP. In particular, optimum solutions could be found up to a problem size of 3795, and for large instances up to 85,900 cities, nearoptimum solutions could be found in a reasonable amount of time
Topologies, Migration Rates, and MultiPopulation Parallel Genetic Algorithms
, 1999
"... This paper presents a study of parallel genetic algorithms (GAs) with multiple populations (also called demes or islands). The study makes explicit the relation between the probability of reaching a desired solution with the deme size, the migration rate, and the degree of the connectivity graph. Th ..."
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Cited by 31 (1 self)
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This paper presents a study of parallel genetic algorithms (GAs) with multiple populations (also called demes or islands). The study makes explicit the relation between the probability of reaching a desired solution with the deme size, the migration rate, and the degree of the connectivity graph. The paper considers arbitrary topologies with a fixed number of neighbors per deme. The demes evolve in isolation until each converges to a unique solution. Then, the demes exchange an arbitrary number of individuals and restart their execution. An accurate demesizing equation is derived, and it is used to determine the optimal configuration of an arbitrary number of demes that minimizes the execution time of the parallel GA. 1 INTRODUCTION Parallel genetic algorithms (GAs) with multiple populations are difficult to configure because they are controlled by many parameters that affect their efficiency and accuracy. Among other things, one must decide the number and the size of the populations...
Inverover Operator for the TSP
 in Proc. PPSN
, 1998
"... gt�rjgc.whu.edu.cn Abstract. In this paper we investigate the usefulness of a new opera� tor � inver�over � for an evolutionary algorithm for the TSP. Inver�over is based on simple inversion � however � knowledge taken from other indi� viduals in the population in�uences its action. Thus � on one ha ..."
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Cited by 25 (0 self)
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gt�rjgc.whu.edu.cn Abstract. In this paper we investigate the usefulness of a new opera� tor � inver�over � for an evolutionary algorithm for the TSP. Inver�over is based on simple inversion � however � knowledge taken from other indi� viduals in the population in�uences its action. Thus � on one hand � the proposed operator is unary � since the inversion is applied to a segment of a single individual � however � the selection of a segment to be inverted is population driven � thus the operator displays some characterictics of recombination. This operator outperforms all other �genetic � operators � whether unary or binary � which have been proposed in the past for the TSP in connection with evolutionary systems and the resulting evolutionary algorithm is very fast. For test cases � where the number of cities is around 100 � the algorithm reaches the optimum in every execution in a couple of seconds. For larger instances �e.g. � 10�000 cities � the results stay within 3 � from the estimated optimum. 1
Asparagos96 and the Traveling Salesman Problem
, 1997
"... This paper describes a spatially structured evolutionary algorithm being applied to the symmetric and asymmetric traveling salesman problem (TSP). This approach shows that a genetic algorithm with high degree of isolationbydistance in combination with a simple repairing mechanism is able to find h ..."
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Cited by 24 (0 self)
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This paper describes a spatially structured evolutionary algorithm being applied to the symmetric and asymmetric traveling salesman problem (TSP). This approach shows that a genetic algorithm with high degree of isolationbydistance in combination with a simple repairing mechanism is able to find high quality solutions for the TSP. The evolutionary part of the algorithm presented differs from the original version of Asparagos [1] in the choice of the topological pattern being now a ring structure and the support of hierarchy. The application part in contrast has been revised in more depth. A new representation which we call the bidirectional array representation [2] is used for the TSP. This representation is invariant concerning the starting point of a tour and allows the realization of a kOpt move in O(1). Our crossover operator MPX is slightly modified in the sense that if there are differing edges in the parent tours, it is now guaranteed that at least one differing edge will oc...