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17th International Computer Science and Engineering Conference (ICSEC)

作者： Jewajinda, Yutana National Electronics and Computer Technology Center|c|

ISBN: (纸本)9781467353229

This paper presents a performance evaluation between hardware and software implementation of a probabilistic parallel genetic algorithm. The compact genetic algorithm is extended to support parallel implementation. The parallelized compact genetic algorithm is implemented in FPGA hardware and parallelized software version running on multi-core processors for performance evaluation using standard benchmark functions. The experimental results show that the hardware implementation of the parallel compact genetic algorithm delivers speedup of between 100-fold to 500-fold depending on problems size and number of generations.

关键词： parallel genetic algorithm FPGA Multi-core processor

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parallel genetic algorithm with fading selection

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INTERNATIONAL JOURNAL OF COMPUTER APPLICATIONS IN TECHNOLOGY 2014年第3-4期49卷 325-331页

作者： Akopov, Andranik S. Natl Res Univ Fac Business Informat Higher Sch Econ Business Analyt Dept Kirpichnaya Str 33 Moscow 105187 Russia

This work presents a novel approach to designing the parallel genetic algorithm (GA) with fading selection for the solving of the problem of the shareholder value maximisation of an oil company. The algorithm based on the dynamical interaction of synchronised processes, which are interdependent GAs having own separate evolutions of their populations. The developed system allows users to find a set of non-dominated investment projects (Pareto-efficient solutions), allowing to pick a particular solution in accordance with their utility function.

关键词： parallel genetic algorithm multi-objective optimisation Pareto-front system dynamics oil company

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An investigation into berth allocation and quay crane assignment based on hybrid parallel genetic algorithm

An investigation into berth allocation and quay crane assign...

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International Conference on Artificial Intelligence and Computational Intelligence

作者： He, Junliang Mi, Weijian Chang, Daofang Yan, Wei Shanghai Maritime Univ Logist Engn Sch Shanghai 200135 Peoples R China Shanghai Jiao Tong Univ Sch Mech Engn Shanghai 200130 Peoples R China

ISBN: (纸本)9780769538167

A rolling-horizon approach was proposed, which aims at the problem of berth allocation and quay crane assignment. Then a dynamic allocation model using objective programming was initially developed for berths allocation and quay crane assignment, which more closes realistic as result of basing continuum quayside. The model objective function was subject to the minimization of the total berthing location deviation, the total penalty and the energy consumption of quay cranes. Then, a hybrid parallel genetic algorithm (HPGA) was employed for solving the model, which combines parallel genetic algorithm (PGA) and heuristic algorithm. Furthermore, a simulation model integrating HPGA was developed for evaluating this HPGA and executing gene repair techniques to repair the unfeasible individuals generated by HPGA. Finally, case study on a specific container terminal was used for system illustration, and then verified the validity and usefulness of this model and algorithm.

关键词： berth allocation quay crane assignment objective programming rolling-horizon technique heuristic algorithm parallel genetic algorithm

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Enhancement of Coarse-Grained parallel genetic algorithm for shortest path Routing

Enhancement of Coarse-Grained Parallel Genetic Algorithm for...

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4th International Conference on Computing, Communications and Networking Technologies (ICCCNT)

作者： Venkatesan, L. Sivakumar, P. SKP Engn Coll Dept EEE Tiruvannamalai Tamil Nadu India SKP Engn Coll Dept ECE Tiruvannamalai Tamil Nadu India

ISBN: (纸本)9781479939251

In general efficient way of routing method is used to transfer the data. This routing problem is solved by using Different types of routing algorithms, here we use Coarse-Grained parallel GA-Based shortest path algorithm. Time computation is the vital parameter in all routing methods. The very shortest path routing algorithm involves reduce time of the transferring data. Using genetic algorithm we can find an efficient path. This algorithm found by using the nature of the genetic operation. genetic algorithm is used to change the genes from one sub-population to another sub-population in a proper manner. In this paper discussion is going through both simple genetic algorithm and parallel genetic algorithm and compares performance of both. Here Migration strategy is used to replace the genes. There are four types of strategies that are used to change the genes. These are: Best replace Worst, Best replace Random, Random replace Random, Random replace Worst, Random replace Random. Among these four types of Strategies worst replace best gives the better performance.

关键词： Simple genetic algorithm parallel genetic algorithm Shortest Path routing Gene's crossover

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The Analysis and Research of parallel genetic algorithm

The Analysis and Research of Parallel Genetic Algorithm

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4th International Conference on Wireless Communications, Networking and Mobile Computing

作者： Xue Shengjun Guo Shaoyong Bai Dongling Nanjing Univ Informat Sci & Technol Comp & Software Inst Nanjing Peoples R China Wuhan Univ Technol Sch Comp Sci & Technol Wuhan Peoples R China

ISBN: (纸本)9781424421077

With the application of the genetic algorithm (GA) deeply developed, the research of parallel genetic algorithm (PGA) and its realization become very important. Because of PGA inner parallel mechanism, its parallel process becomes a very naturally resolvable method. In this paper, four kinds of parallel models of parallel genetic algorithms, such as master-slave model, coarse-grained model, fine-grained model and mixed model, are simply generalized and evaluated. For every model, its characteristics are displayed. As for the existing problem to each model, the concerning parameters are illustrated in order to improve them. Then some main evaluation models of parallel genetic algorithms are presented. At the end, it is shown that parallel genetic algorithms should go on further study in the future.

关键词： genetic algorithm parallel genetic algorithm mixed model evaluation

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A Improved parallel genetic algorithm Based on Fixed Point Theory for the Optimal Design of Multi-body Model Vehicle Suspensions

A Improved Parallel Genetic Algorithm Based on Fixed Point T...

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2nd IEEE International Conference on Computer Science and Information Technology

作者： Liu, Guangyuan Zhang, Jingjun Gao, Ruizhen Sun, Yang Hebei Univ Engn Sci Res Off Handan Peoples R China Hebei Univ Engn Coll Elect & Informat Engn Handan Peoples R China

ISBN: (纸本)9781424445189

Based on an improved genetic algorithm, a parallel genetic algorithm is presented and the skeleton implementing is constituted in this paper. The van der Laan-Talman algorithm is introduced to the genetic algorithm to design convergence criteria objectively and to solve the convergence problem in the later period. The parallel genetic algorithm of multi-body model vehicle suspension optimization is implemented through establishing the interface between ADAMS software and the genetic algorithm. The results show that the parallel genetic algorithm developed in this paper is efficient.

关键词： Vehicle suspension genetic algorithm parallel genetic algorithm Fixed Point Theory Cluster system

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Integration of parallel genetic algorithm and Q-learning for QoS-aware Web Service Composition 12

Integration of Parallel Genetic Algorithm and Q-learning for...

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12th International Conference on Computer Engineering and Systems (ICCES)

作者： Elsayed, Doaa H. Gheith, Mervat H. Nasr, Eman S. El Ghazali, Alaa El Din M. Cairo Univ ISSR Dept Informat Syst Technol Cairo Egypt Cairo Univ ISSR Dept Comp Sci Cairo Egypt Sadat Acad Management Sci Dept Comp & Informat Syst Cairo Egypt

ISBN: (纸本)9781538611913

Web Service Composition (WSC) is the process of reusing atomic Web services and combining them together to satisfy users' requirements. The main objective of WSC is to develop composite services to satisfy the Functional Requirements (FR), as well as optimizing the Quality of Services (QoS) requirements. This has led to the emergence of QoS-aware WSC. Due to the increase in number of Web services with the same functionality but various QoS, it became difficult to find the optimal solution in QoS-aware WSC in a given time frame. In this paper we propose a new approach that integrates the use of the parallel genetic algorithm (PGA) and Q-learning to find the optimal WSC within reasonable time. Q-learning is used to generate the initial population to enhance the effectiveness of PGA. PGA is utilized to make the algorithm as time efficient as possible. We implemented our approach *** Framework platform 4.7 using C# programming language. The experiment results show the effectiveness of our proposed approach compared to PGA or GA only.

关键词： quality of services Web service composition parallel genetic algorithm Q-learning

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Optimal Network Hardening Model Based on parallel genetic algorithm

Optimal Network Hardening Model Based on Parallel Genetic Al...

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International Conference on Industrial Control and Electronics Engineering (ICICEE)

作者： Ma Jun-chun Sun Ji-yin 402 Staff Second Artillery Engn Inst Xian 710025 Peoples R China

ISBN: (纸本)9780769547923

Because the existed approaches to harden networks have an unavoidable exponential worse-case complexity, and are not scalable to large networks, this paper proposes an optimal network hardening model (ONHM) based on parallel genetic algorithm by combining attack graphs and parallel genetic algorithm. Firstly, we describe the optimal network hardening problem;secondly, we establish a corresponding mathematical model, which converts the optimal network hardening problem to a non-restraint optimization problem with penalty. Through a large number of repeated laboratory tests, the experimental results show ONHM can find the optimal network hardening, and can be applied to large-scale networks.

关键词： network security vulnerability attack graphs optimal network hardening parallel genetic algorithm

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Application of parallel genetic algorithm with two-step fitness function forthe design of multi-band single-layer frequency selective surface

Application of parallel genetic algorithm with two-step fitn...

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IEEE International Symposium on Microwave, Antenna, Propagation and EMC Technologies for Wireless Communications

作者： Zhou, R. Zhuang, W. Ding, D. Z. Chen, R. S. Nanjing Univ Sci & Technol Dept Commun Engn Nanjing 210094 Peoples R China

ISBN: (纸本)9781424410446

The parallel genetic algorithm (PGA) with two-step fitness function is proposed to design the multi-band single-layer frequency selective surface (FSS). The inner-outer flexible generalized minimal residual algorithm combined with fast Fourier transform (FGMRES-FFT) method is used to accelerate the convergence of the system equation of method of moment. An example of the tri-band single-layer FSS structure is given to demonstrate the validity of the present method.

关键词： frequency selective surface parallel genetic algorithm fitness function FGMRES-FFT

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An Enhanced MapReduce Framework for Solving Protein Folding Problem Using a parallel genetic algorithm

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48th Annual Convention of Computer Society of India (CSI)

作者： Narayanan, A. G. Hari Krishnakumar, U. Judy, M. V. Amrita Vishwa Vidyapeetham Amrita Sch Arts & Sci Dept Comp Sci & IT Kochi Kerala India

ISBN: (纸本)9783319031071

parallel genetic algorithms have proved to be a successful method for solving the protein folding problem. In this paper we propose a simple genetic algorithm with optimum population size, mutation rate and selection strategy which is parallelized with MapReduce architecture for finding the optimal conformation of a protein using the two dimensional square HP model. We have used an enhanced framework for map Reduce which increased the performance of the private clouds in distributed environment. The proposed genetic algorithm was tested several bench mark of synthetic sequences. The result shows that GA converges to the optimum state faster than the traditional

关键词： Protein Folding Problem Hadoop MapReduce parallel genetic algorithm

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