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Heterogeneous computing and parallel genetic algorithms

JOURNAL OF parallel AND DISTRIBUTED COMPUTING 2002年第9期62卷 1362-1385页

作者： Alba, E Nebro, AJ Troya, JM Univ Malaga Dept Lenguajes & Ciencias Computac ETSI Informat E-29071 Malaga Spain

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)

关键词： parallel genetic algorithms Java heterogeneous computational systems speedup

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Designing digital circuits for FPGAs using parallel genetic algorithms (WIP)

Designing digital circuits for FPGAs using parallel genetic ...

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Theory of Modeling and Simulation - DEVS Integrative M&S Symposium (DEVS 2012)

作者： Ashraf, Rizwan A. Luna, Francis Dechev, Damian DeMara, Ronald F. Univ Cent Florida Dept Elect Engn & Comp Sci Orlando FL 32816 USA

ISBN: (纸本)9781618397867

Multicore processors are becoming common whereas current genetic algorithm-based implementation techniques for synthesizing Field Programmable Gate Array (FPGA) circuits do not fully exploit this hardware trend. genetic Algorithm (GA) based techniques are known to optimize multiple objectives, and automate the process of digital circuit design. In this paper, parallel GA algorithms are proposed for the synthesis of digital circuits for LUT-based FPGA architectures. parallel modes of the GA such as Master-Slave and the Island model are compared to see which scheme results in better speedup and quicker convergence for effective utilization of current multicore hardware. Speedup of about five over the sequential single-threaded implementation is achieved with both the schemes on a six-core machine. Convergence is also found in fewer number of generations. The methods described here-in can be employed in Evolvable Hardware Systems as well as FPGA CAD tools.

关键词： FPGA Circuit Synthesis parallel genetic algorithms Evolvable Hardware Multicore Computing

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Develop, Deploy and Execute parallel genetic algorithms in the Cloud

Develop, Deploy and Execute Parallel Genetic Algorithms in t...

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genetic and Evolutionary Computation Conference (GECCO)

作者： Salza, Pasquale Ferrucci, Filomena Sarro, Federica Univ Salerno Fisciano Italy UCL London WC1E 6BT England

Making genetic algorithms (GAs) distributed in an on demand fashion involves different phases from resources allocation to actual deployment and execution. We propose a cloud architecture with a conceptual workflow ab... 详细信息

ISBN: (纸本)9781450343237

关键词： parallel genetic algorithms Cloud Computing DevOps Container Virtualisation

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Fuzzy controller design by parallel genetic algorithms

Fuzzy controller design by parallel genetic algorithms

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SPIE International Conference on Applications and Science of Computational Intelligence

作者： Mondelli, G Castellano, G Attolico, G Distante, A CNR IESI I-70126 Bari Italy

ISBN: (纸本)0819428396

Designing a fuzzy system involves defining membership functions and constructing rules. Carrying out these two steps manually often results in a poorly performing system. genetic algorithms (GAs) has proved to be a useful tool for designing optimal fuzzy controller. In order to increase the efficiency and effectiveness of their application, parallel GAs (PAGs), evolving synchronously several populations with different balances between exploration and exploitation, have been implemented using a SIMD machine (APE100/Quadrics). The parameters to be identified are coded in such a way that the algorithm implicitly provides a compact fuzzy controller, by finding only necessary rules and removing useless inputs from them. Early results, working on a fuzzy controller implementing the wall-following task for a real vehicle as a test case, provided better fitness values in less generations with respect to previous experiments made using a sequential implementation of GAs.

关键词： fuzzy controller design parallel genetic algorithms robotic control

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A parallel genetic algorithms Framework based on Hadoop MapReduce 15

A Parallel Genetic Algorithms Framework based on Hadoop MapR...

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30th ACM Symposium on Applied Computing (SAC)

作者： Ferrucci, Filomena Salza, Pasquale Kechadi, M-Tahar Sarro, Federica Univ Salerno Fisciano Sa Italy Univ Coll Dublin Dublin Ireland UCL London WC1E 6BT England

ISBN: (纸本)9781450331968

This paper describes a framework for developing parallel genetic algorithms (GAs) on the Hadoop platform, following the paradigm of MapReduce. The framework allows developers to focus on the aspects of GA that are specific to the problem to be addressed. Using the framework a GA application has been devised to address the Feature Subset Selection problem. A preliminary performance analysis showed promising results.

关键词： Hadoop MapReduce parallel genetic algorithms

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elephant56: Design and Implementation of a parallel genetic algorithms Framework on Hadoop MapReduce

elephant56: Design and Implementation of a Parallel Genetic ...

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genetic and Evolutionary Computation Conference (GECCO)

作者： Salza, Pasquale Ferrucci, Filomena Sarro, Federica Univ Salerno Fisciano SA Italy UCL London WC1E 6BT England

ISBN: (纸本)9781450343237

elephant56 is an open source framework for the development and execution of single and parallel genetic algorithms (GAs). It provides high level functionalities that can be reused by developers, who no longer need to worry about complex internal structures. In particular, it offers the possibility of distributing the GAs computation over a Hadoop MapReduce cluster of multiple computers. In this paper we describe the design and the implementation details of the framework that supports three different models for parallel GAs, namely the global model, the grid model and the island model. Moreover, we provide a complete example of use.

关键词： parallel genetic algorithms Hadoop MapReduce Cloud Computing

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Applications of parallel genetic algorithms in Ground Water Contamination Problems: An Overview

Applications of Parallel Genetic Algorithms in Ground Water ...

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World Congress on Nature and Biologically Inspired Computing

作者： Sharma, Sunita Indian Inst Technol Dept Math Roorkee 247667 Uttarakhand India

ISBN: (纸本)9781424450534

Groundwater resource management is a challenging problem faced by almost all the countries. Mathematical models of these problems often turn out to be illdefined subject to several variables and constraints. Sophisticated algorithms are needed in order to deal efficiently with such problems. In the past few decades much attention has been paid to heuristic techniques like genetic algorithms etc which can easily solve such problems. Further, in order to tackle the large number of involved parameters in these problems parallel version of GAs is more effective than the basic GAs. In this paper an attempt is made to review the application of PGA on groundwater management problems.

关键词： genetic algorithms parallel genetic algorithms Ground water contamination problems

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Predicting mining activity with parallel genetic algorithms 05

Predicting mining activity with parallel genetic algorithms

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genetic and Evolutionary Computation Conference

作者： Talaie, Sam Leigh, Ryan Louis, Sushil J. Raines, Gary L. Univ Nevada Dept Comp Sci & Engn Evolutionary Comp Syst Lab Reno NV 89557 USA

ISBN: (纸本)1595930108

We explore several different techniques in our quest to improve the overall model performance of a genetic algorithm calibrated probabilistic cellular automata. We use the Kappa statistic to measure correlation between ground truth data and data predicted by the model. Within the genetic algorithm, we introduce a new evaluation function sensitive to spatial correctness and we explore the idea of evolving different rule parameters for different subregions of the land. We reduce the time required to run a simulation from 6 hours to 10 minutes by parallelizing the code and employing a 10-node cluster. Our empirical results suggest that using the spatially sensitive evaluation function does indeed improve the performance of the model and our preliminary results also show that evolving different rule parameters for different regions tends to improve overall model performance.

关键词： parallel genetic algorithms cellular automata

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On-line computation of Stackelberg equilibria with synchronous parallel genetic algorithms

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JOURNAL OF ECONOMIC DYNAMICS & CONTROL 2003年第8期27卷 1503-1515页

作者： Alemdar, NM Sirakaya, S Univ Wisconsin Dept Econ Madison WI 53706 USA Bilkent Univ Dept Econ TR-06533 Bilkent Ankara Turkey

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.

关键词： Stackelberg equilibrium parallel genetic algorithms feed-forward neural networks

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A cross-platform parallel genetic algorithms programming environment

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parallel algorithms and Applications 1995年第3-4期7卷 255-266页

作者： Tout, K. Ribeiro-Filho, J. Mignot, B. HERMED GmbH 24536 Neumünster Ilsahl 5 Germany University College London Dept. of Computer Science London WC1E 6BT United Kingdom Telmat Multinode BP 12 F-68360 Soultz 6 rue de l'industrie France

genetic algorithms (GAs) have been widely and successfully used for solving complex optimisation problems. This paper presents the research being conducted under the ESPRIT III PAPAGENA project which aims to establish an European standard for the design of adaptive genetic systems. It describes the architecture of the genetic algorithms Manipulation Environment (GAME). GAME is, a parallel, general-purpose object-oriented programming environment that offers extensive tools for the design, configuration and monitoring of parallel genetic Algorithm-based (PGA) applications in a variety of do mains. To exploit the intrinsic parallelism of GAs, two parallelisation options are available within GAME: Implicit, where the parallelism is low-level and transparent to the application developer;and explicit in which the parallelism is managed by the application developer, and could be introduced at different levels and in different ways. The parallel layer of GAME is hardware platform-independent. This property means that it is portable on a set of currently available parallel platforms. A special consideration has been given for the implementation on parallel architectures based on T800 and T9000 transputers. © 1995, Taylor & Francis Group, LLC. All rights reserved.

关键词： Object-oriented programming parallel genetic algorithms Toolkit Transputer

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