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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 222 (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.
On Evolution, Search, Optimization, Genetic Algorithms and Martial Arts  Towards Memetic Algorithms
, 1989
"... Short abstract, isn't it? P.A.C.S. numbers 05.20, 02.50, 87.10 1 Introduction Large Numbers "...the optimal tour displayed (see Figure 6) is the possible unique tour having one arc fixed from among 10 655 tours that are possible among 318 points and have one arc fixed. Assuming that one could ..."
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Cited by 187 (10 self)
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Short abstract, isn't it? P.A.C.S. numbers 05.20, 02.50, 87.10 1 Introduction Large Numbers "...the optimal tour displayed (see Figure 6) is the possible unique tour having one arc fixed from among 10 655 tours that are possible among 318 points and have one arc fixed. Assuming that one could possibly enumerate 10 9 tours per second on a computer it would thus take roughly 10 639 years of computing to establish the optimality of this tour by exhaustive enumeration." This quote shows the real difficulty of a combinatorial optimization problem. The huge number of configurations is the primary difficulty when dealing with one of these problems. The quote belongs to M.W Padberg and M. Grotschel, Chap. 9., "Polyhedral computations", from the book The Traveling Salesman Problem: A Guided tour of Combinatorial Optimization [124]. It is interesting to compare the number of configurations of realworld problems in combinatorial optimization with those large numbers arising in Cosmol...
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.
SearchIntensive Concept Induction
, 1995
"... This paper describes REGAL, a distributed genetic algorithmbased system, designed for learning First Order Logic concept descriptions from examples. The system is a hybrid between the Pittsburgh and the Michigan approaches, as the population constitutes a redundant set of partial concept descriptio ..."
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Cited by 77 (3 self)
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This paper describes REGAL, a distributed genetic algorithmbased system, designed for learning First Order Logic concept descriptions from examples. The system is a hybrid between the Pittsburgh and the Michigan approaches, as the population constitutes a redundant set of partial concept descriptions, each evolved separately. In order to increase effectiveness, REGAL is specifically tailored to the concept learning task; hence, REGAL is taskdependent, but, on the other hand, domainindependent. The system proved to be particularly robust with respect to parameter setting across a variety of different application domains. REGAL is based on a selection operator, called Universal Suffrage operator, provably allowing the population to asymptotically converge, in average, to an equilibrium state, in which several species coexist. The system is presented both in a serial and in a parallel version, and a new distributed computational model is proposed and discussed. The system has been test...
The Ariadne's clew algorithm
, 1996
"... We present a general planning strategy to plan the motions of an agent having to explore a continuous state space in order to reach one or several goals. We propose a practical method to implement this technique based on a genetic algorithm and we illustrate the approach on the problem of controllin ..."
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Cited by 73 (3 self)
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We present a general planning strategy to plan the motions of an agent having to explore a continuous state space in order to reach one or several goals. We propose a practical method to implement this technique based on a genetic algorithm and we illustrate the approach on the problem of controlling a mobile robot moving in a maze and looking for several items. Finally, we show that this planning strategy may serve as a possible control structure for an autonomous system. Problem Solving and planning 1 Introduction This study was motivated by our previous work on robot motion planning using a parallel genetic algorithm [8]. The planner we have design and implemented on a parallel machine is capable of planning collision free paths for a mobile robot placed among obstacles. The main advantage of this planner is its speed, it can plan complex paths such as the two paths represented in figure 1 in less than 0.5 second on a parallel machine with 64 Transputers. As a consequence it can b...
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.
The distributed genetic algorithm revisited
 Proceedings of the Sixth International Conference on Genetic Algorithms
, 1995
"... This paper extends previous work done by Tanese on the distributed genetic algorithm (DGA). Tanese found that the DGA outperformed the canonical serial genetic algorithm (CGA) on a class of di cult, randomlygenerated Walsh polynomials. This left open the question of whether the DGA would have simila ..."
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Cited by 65 (0 self)
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This paper extends previous work done by Tanese on the distributed genetic algorithm (DGA). Tanese found that the DGA outperformed the canonical serial genetic algorithm (CGA) on a class of di cult, randomlygenerated Walsh polynomials. This left open the question of whether the DGA would have similar success on functions that were more amenable to optimization by the CGA. In this work, experiments were done to compare the DGA's performance on the Royal Road class of tness functions to that of the CGA. Besides achieving superlinear speedup on KSR parallel computers, the DGA again outperformed the CGA on the functions R3 and R4 with regard to the metrics of best tness, average tness, and number of times the optimum was reached. Its performance on 1 and 2 was comparable to that of the CGA. The e ect of varying the DGA's migration parameters was also investigated. The results of the experiments are presented and discussed, and suggestions for future research are made. R R i
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...
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 ..."
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Cited by 35 (5 self)
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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...