This paper develops a method to compute the Stackelberg equilibria in sequential games. We construct a normal form game which is interactively played by an artificially intelligent leader, GAL, and a follower, GA(F). ...
详细信息
This paper develops a method to compute the Stackelberg equilibria in sequential games. We construct a normal form game which is interactively played by an artificially intelligent leader, GAL, and a follower, GA(F). The leader is a genetic algorithm breeding a population of potential actions to better anticipate the follower's reaction. The follower is also a genetic algorithm training on-line a suitable neural network to evolve a population of rules to respond to any move in the leader's action space. When GAs repeatedly play this game updating each other synchronously, populations converge to the Stackelberg equilibrium of the sequential game. We provide numerical examples attesting to the efficiency of the algorithm. (C) 2002 Elsevier Science B.V. All rights reserved.
The VLSI building block layout(BBL) becomes a more and more important problem for VLSI physical design. In this paper, A Multithread scheme for parallelizing a genetic algorithm for BBL placement optimization is prese...
详细信息
ISBN:
(纸本)9781424405169
The VLSI building block layout(BBL) becomes a more and more important problem for VLSI physical design. In this paper, A Multithread scheme for parallelizing a genetic algorithm for BBL placement optimization is presented. The parallel genetic algorithms(PGA) are realized, using sequence-pair(SP) as the representation. parallel. algorithm can be used either to speed up a problem or to achieve a higher accuracy of solutions to a problem. Our experimental results on a SUN workstation with 4 CPUs have shown that the scheme is effective in improving performance of placement over that of a sequential implementation.
In this paper, we present a system-level fault identification algorithm, using a parallelgenetic algorithm, for diagnosing faulty nodes in large heterogeneous systems. The algorithm is based on a probabilistic model ...
详细信息
In this paper, we present a system-level fault identification algorithm, using a parallelgenetic algorithm, for diagnosing faulty nodes in large heterogeneous systems. The algorithm is based on a probabilistic model where individual node fails with an a priori probability p. The assumptions concerning test outcomes are the same as in the PMC model, that is, fault-free testers always give correct test outcomes and faulty testers are totally unpredictable. The parallel diagnosis algorithm was implemented and simulated on randomly generated large systems. The proposed parallelization is intended to speed up the performance of the evolutionary diagnosis approach, hence reducing the computation time by evolving various sub-populations in parallel. Simulation results are provided showing that the parallel diagnosis did improve the efficiency of the evolutionary diagnosis approach, in that it allowed faster diagnosis of faulty situations, making it a viable alternative to existing techniques of diagnosis. Moreover, the evolutionary approach still provide good results even when extreme non-diagnosable faulty situations are considered.
parallel genetic algorithms (PGAs) have been developed to reduce the execution time of serial geneticalgorithms (SGA) and to solve larger problems. They typically find better solutions, as they are likely to be more ...
详细信息
ISBN:
(纸本)0889865566
parallel genetic algorithms (PGAs) have been developed to reduce the execution time of serial geneticalgorithms (SGA) and to solve larger problems. They typically find better solutions, as they are likely to be more resistant to premature convergence to local minima compared to serial geneticalgorithms. This paper presents a comparative study of four different PGAs using the traveling salesman problem (TSP) as the case application in order to quantify their performance on the basis of small initial populations. Besides the well-known parallelization approaches, a new parallelization scheme is considered which combines iterative data exchanges and new solutions generation, thus extending a search space during the evolution. To make the comparison fair, all PGAs are using the same baseline serial genetic algorithm, started from the same set of initial populations and tested on the same known instances from the TSPLIB-archive.
The VLSI building block layout(BBL) becomes a more and more important problem for VLSI physical design. In this paper, A Multithread scheme for parallelizing a genetic algorithm for BBL placement optimization is prese...
详细信息
The VLSI building block layout(BBL) becomes a more and more important problem for VLSI physical design. In this paper, A Multithread scheme for parallelizing a genetic algorithm for BBL placement optimization is presented. The parallel genetic algorithms(PGA) are realized, using sequence-pair(SP) as the representation. parallel algorithm can be used either to speed up a problem or to achieve a higher accuracy of solutions to a problem. Our experimental results on a SUN workstation with 4 CPUs have shown that the scheme is effective in improving performance of placement over that of a sequential implementation.
geneticalgorithms are among of the global optimization schemes that have gained popularity as a means to calibrate rainfall-runoff models. However, a conceptual rainfall-runoff model usually includes 10 or more param...
详细信息
geneticalgorithms are among of the global optimization schemes that have gained popularity as a means to calibrate rainfall-runoff models. However, a conceptual rainfall-runoff model usually includes 10 or more parameters and these are interdependent, which makes the optimization procedure very time-consuming. This may result in the premature termination of the optimization process which will prejudice the quality of the results. Therefore, the speed of optimization procedure is crucial in order to improve the calibration quality and efficiency. A hybrid method that combines a parallelgenetic algorithm with a fuzzy optimal model in a cluster of computers is proposed. The method uses the fuzzy optimal model to evaluate multiple alternatives with multiple criteria where chromosomes are the alternatives, whilst the criteria are flood performance measures. In order to easily distinguish the performance of different alternatives and to address the problem of non-uniqueness of optimum, two fuzzy ratios are defined. The new approach has been tested and compared with results obtained by using a two-stage calibration procedure. The current single procedure produces similar results, but is simpler and automatic. Comparison of results between the serial and parallel genetic algorithms showed that the current methodology can significantly reduce the overall optimization time and simultaneously improve the solution quality.
This paper presents a general model to define, measure and predict the efficiency of applications running on heterogeneous parallel computer systems. Using this framework, it is possible to understand the influence th...
详细信息
This paper presents a general model to define, measure and predict the efficiency of applications running on heterogeneous parallel computer systems. Using this framework, it is possible to understand the influence that the heterogeneity of the hardware has on the efficiency of an algorithm. This methodology is used to compare an existing parallelgenetic algorithm with a new adaptive parallel model. All the performance measurements were taken in a loosely coupled cluster of processors. (C) 2004 Elsevier Inc. All rights reserved.
This paper presents a comparative study of five different coarse-grained parallel genetic algorithms (PGAs) using the traveling salesman problem as the case application. All of these PGAs are based on the same baselin...
详细信息
This paper presents a comparative study of five different coarse-grained parallel genetic algorithms (PGAs) using the traveling salesman problem as the case application. All of these PGAs are based on the same baseline serial genetic algorithm, implemented on the same parallel machine (IBM SP2), tested on the same problem instances, and started from the same set of initial populations. Based on these experiments, a PGA that combines a new subtour technique with a known migration approach is identified to be the best for the traveling salesman problem among the five PGAs being compared.
This paper deals with the problem of fault identification in large diagnosable systems under the PMC model. Recently, geneticalgorithms have been successfully used to solve this system-level fault diagnosis problem;h...
详细信息
This paper deals with the problem of fault identification in large diagnosable systems under the PMC model. Recently, geneticalgorithms have been successfully used to solve this system-level fault diagnosis problem;however, they have one major drawback, i.e. even though they have been shown to perform better than the existing diagnosis algorithms, they are still time-consuming especially for large systems composed of hundreds or thousands of nodes. In this paper, we describe a new parallel version of the existing evolutionary diagnosis algorithm, which exploits competing sub-populations to speed up the diagnosis algorithm. The new approach has been implemented using the parallel virtual machine (PVM) environment and has been evaluated on a workstation network using randomly generated large diagnosable systems. Experimental results showed that the new parallel version considerably improved the response time of the diagnosis algorithm, hence, allowing for fast identification of faulty nodes.
This paper analyzes some technical and practical issues concerning the heterogeneous execution of parallel genetic algorithms (PGAs). In order to cope with a plethora of different operating systems, security restricti...
详细信息
This paper analyzes some technical and practical issues concerning the heterogeneous execution of parallel genetic algorithms (PGAs). In order to cope with a plethora of different operating systems, security restrictions, and other problems associated to multi-platform execution. we use Java to implement a distributed PGA model. The distributed PGA runs at the same time on different machines linked by different kinds of communication networks. This algorithm benefits from the computational resources offered by modern LANs and by Internet, therefore allowing researchers to solve more difficult problems by using a large set of available machines. We analyze the way in which such heterogeneous systems affect the genetic search for two problems. Our conclusion is that super-linear performance can be achieved not only in homogeneous but also in heterogeneous clusters of machines. In addition, we study some special features of the running platforms for PGAs, and basically find out that heterogeneous computing can be as efficient or even more efficient than homogeneous computing for parallel heuristics. (C) 2002 Elsevier Science (USA)
暂无评论