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Analysis of crossovers and selections in a coarse-grained parallel genetic algorithm

MATHEMATICAL AND COMPUTER MODELLING 2003年第11-13期38卷 1275-1282页

作者： Katayama, K Hirabayashi, H Narihisa, H Okayama Univ Sci Dept Informat & Comp Engn Okayama 7000005 Japan Matsushita Syst & Technol Co Ltd Kyoto 6178520 Japan

The parallel genetic algorithms (PGA) have been developed for combinatorial optimization problems, and its parallel efficiencies have been investigated on a specific problem. These investigations were. concerned with how to design a topology and the determination of the optimum setting for parameters (for example, size of subpopulations, migration interval, and so on) rather than the effectiveness of genetic operators. This paper investigates a relation between the parallel efficiency of the coarse-grained PGA and genetic (crossover and selection) operators for the traveling salesman problem on an MIMD parallel computer. The following genetic operators are considered: improved edge recombination (IERX), distance preserving (DPX), and complete subtour exchange (CSEX) crossovers, and two selection operators, which have relatively high selection pressures. Computational results indicate that the parallel efficiency is significantly affected by the difference of crossovers rather than the selections, and the PGA with CSEX gives better properties. (C) 2003 Elsevier Ltd. All rights reserved.

关键词： parallel genetic algorithm parameter determination traveling salesman problem parallel computer genetic operators

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A combined parallel genetic algorithm and support vector machine model for breast cancer detection

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JOURNAL OF COMPUTATIONAL METHODS IN SCIENCES AND ENGINEERING 2016年第4期16卷 773-785页

作者： Xu, Hongyan Chen, Ting Lv, Junmin Guo, Jin SouthWestern Univ Finance & Econ Coll Tianfu Mianyang Sichuan Peoples R China

The serial genetic algorithms (SGAs) have been widely applied in improving support vector machine (SVM) performance (e.g., classification accuracy), and these hybrid SGA-SVM methods show good capability to detect breast cancer. However, there remain two great challenges: (1) the improvements tend to be at the great cost of time-consuming training;and (2) the SGA-based search may risk the premature convergence to local optima and thereby decrease the quality of the solutions found. The study aimed to investigate the use of parallel genetic algorithms (PGAs) in improving SVM performance, and build an efficient and accurate classifier of detecting breast cancer. A coarse-grained parallel genetic algorithm (CGPGA) was used to select a feature subset and optimize the parameters of SVM simultaneously. This approach (CGPGA-SVM) was then applied to a well characterized breast cancer dataset, consisting of 699 samples (458 benign and 241 malignant samples). In addition, the proposed CGPGA-SVM classier was compared with a range of SVM-based classifiers to understand its performance improvements. Compared with the SGA-SVM classifier, the training time of the CGPGA-SVM classier decreased by 75.77% on a commonly used 4-core CPU;moreover, the classification accuracy and sensitivity of the CGPGA-SVM classifier increased by 0.43% and 1.25%, respectively.

关键词： parallel genetic algorithm support vector machine feature selection parameter optimization breast cancer diagnosis

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Dynamic source inversion of the M6.5 intermediate-depth Zumpango earthquake in central Mexico: A parallel genetic algorithm

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JOURNAL OF GEOPHYSICAL RESEARCH-SOLID EARTH 2014年第10期119卷 7768-7785页

作者： Diaz-Mojica, John Cruz-Atienza, Victor M. Madariaga, Raul Singh, Shri K. Tago, Josue Iglesias, Arturo Univ Nacl Autonoma Mexico Inst Geofis Mexico City 04510 DF Mexico Univ Grenoble 1 Inst Sci Terre Grenoble France Ecole Normale Super Geol Lab F-75231 Paris France

We introduce a method for imaging the earthquake source dynamics from the inversion of ground motion records based on a parallel genetic algorithm. The source model follows an elliptical patch approach and uses the staggered-grid split-node method to simulate the earthquake dynamics. A statistical analysis is used to estimate errors in both inverted and derived source parameters. Synthetic inversion tests reveal that the average rupture speed (V-r), the rupture area, and the stress drop () may be determined with formal errors of similar to 30%, similar to 12%, and similar to 10%, respectively. In contrast, derived parameters such as the radiated energy (E-r), the radiation efficiency ((r)), and the fracture energy (G) have larger errors, around similar to 70%, similar to 40%, and similar to 25%, respectively. We applied the method to the M-w 6.5 intermediate-depth (62km) normal-faulting earthquake of 11 December 2011 in Guerrero, Mexico. Inferred values of =29.26.2MPa and (r)=0.260.1 are significantly higher and lower, respectively, than those of typical subduction thrust events. Fracture energy is large so that more than 73% of the available potential energy for the dynamic process of faulting was deposited in the focal region (i.e., G=(14.43.5)x10(14)J), producing a slow rupture process (V-r/V-S=0.470.09) despite the relatively high energy radiation (E-r=(0.54 +/- 0.31)x10(15)J) and energy-moment ratio (E-r/M-0=5.7x10(-5)). It is interesting to point out that such a slow and inefficient rupture along with the large stress drop in a small focal region are features also observed in both the 1994 deep Bolivian earthquake and the seismicity of the intermediate-depth Bucaramanga nest.

关键词： dynamic rupture inversion heuristic inversion parallel genetic algorithm intraslab Mexican earthquake intermediate-depth earthquake slow inefficient rupture

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Uncertainty analysis of water distribution networks using type-2 fuzzy sets and parallel genetic algorithm

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URBAN WATER JOURNAL 2019年第3期16卷 193-204页

作者： Geranmehr, Mohammadali Asghari, Keyvan Chamani, Mohammad R. Isfahan Univ Technol Dept Civil Engn Esfahan Iran

Water distribution networks are typically designed based on certain values of uncertain parameters such as reservoir head, nodal demand and friction coefficient to furnish acceptable performance by limiting nodal pressure head and pipes' velocities. In this study, a fuzzy uncertainty analysis approach is developed to handle uncertainty of reservoir head and nodal demand over an extended period and long-term simulation, based on type-2 fuzzy logic. Furthermore, the long-term uncertainty of Hazen-Williams (HW) coefficient is also considered. A parallel genetic algorithm is introduced to solve a multi-objective optimization problem for fuzzy uncertainty analysis. The method is powered by a pressure dependent hydraulic simulator which is based on EPANET and an iterative procedure. It is implemented for a benchmark network from literature and a case study. Results show that network velocity is highly affected by uncertainties and the accumulation of different uncertainties may change the network performance significantly.

关键词： Water distribution network parallel genetic algorithm type-2 fuzzy sets uncertainty analysis

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Research on Text Feature Clustering Based on Improved parallel genetic algorithm 10

Research on Text Feature Clustering Based on Improved Parall...

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10th International Conference on Advanced Computational Intelligence (ICACI)

作者： Jiang, Mingyang Fan, Xiaojing Pei, Zhili Zhang, Zhifeng Inner Mongolia Univ Nationalities Coll Comp Sci & Technol Tongliao Peoples R China Inner Mongolia Univ Nationalities Coll Mech Engn Tongliao Peoples R China

ISBN: (纸本)9781538643624

Synonyms, synonyms and the strong association of semantic information increase the dimension text feature vectors, and greatly affect the efficiency and accuracy of text classification. In order to reduce the dimension of the text feature vectors, this paper presents an improved parallel genetic algorithm to sovle the text feature clustering problem. Firstly, the K-means algorithm is used to cluster the feature words. The parallel genetic algorithm is used to fine-grained the feature words. In the process of applying genetic algorithms, crossover operator is improved so that the algorithm has a global search ability and local search capability, and reduce the dependence on the initial cluster centers. Finally, the different types of feature words are analyzed and compressed, and a set of feature words of the text category and the semantic information is formed. The experimental results verify our method of text feature extraction is better.

关键词： parallel genetic algorithm K-means algorithm Text feature extraction

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Scalable parallel genetic algorithm For Solving Large Integer Linear Programming Models Derived From Behavioral Synthesis 28

Scalable Parallel Genetic Algorithm For Solving Large Intege...

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28th Euromicro International Conference on parallel, Distributed and Network-Based Processing (PDP)

作者： Fallah, Mohammad K. Mirhosseini, Mina Fazlali, Mahmood Daneshtalab, Masoud GC Shahid Beheshti Univ Fac Math Sci Dept Data & Comp Sci Tehran Iran Malardalen Univ Intelligent Future Technol Vasteras Sweden

ISBN: (数字)9781728165820

ISBN: (纸本)9781728165820

Solving Integer Linear Programming (ILP) models generally lies in the category of NP-hard problems. Therefore, as the size of ILP models grows, the efficiency of exact algorithms for solving the models reduced significantly and for large models it is not possible to have the result. genetic algorithm (GA) is a metaheuristic method capable of adjusting and redesigning parameters and operations according to the characteristics of ILP models. Still GA has huge search space for large models and parallelization is a suitable technique to tackle this problem. This paper presents a scalable parallel GA to solve large ILP models derived from behavioral synthesis of digital circuits. We show that although models have non-binary variables, only binary variables are sufficient for coding chromosomes. We also use "unknown" values for some genes to decrease the likelihood of inconsistency in the encoded constraints. Our experiments verify the efficiency and scalability of the proposed algorithm on multicore platforms. The proposed method outperforms IBM ILOG CPLEX 12.6 and MI-LXPM algorithm where the ILP models include 550 to 2258 int / binary decision variables. Also, the results indicate that the saturation point of using parallel processing elements for solving the large ILP models is at least 60.

关键词： behavioral synthesis integer linear programming parallel genetic algorithm

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A new model of global parallel genetic algorithm

A new model of global parallel genetic algorithm

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22nd International Conference on Information Technology Interfaces (ITI 2000)

作者： Golub, M Jakobovic, D Univ Zagreb Fac Elect Engn & Comp Dept Elect Microelect Comp & Intelligent Syst Zagreb 10000 Croatia

ISBN: (纸本)9539676916

In this paper we describe a multithreaded parallel genetic algorithm (PGA) implementation. Considering the basic models of parallel genetic algorithms, we identify, a variant of global PGA (GPGA) as the most appropriate one for use on a microprocessor system with few processors. The difference between the synchronous and asynchronous model is analized and their characteristics are evaluated. Unlike some authors, we choose not to allow a single individual to be engaged in a tournament competition in more than orle instance (no duplicates). The probability of selection for elimination of an individual is than determined based on the fitness of the chromosome and compared with the same probability of the duplicate-allowing algorithm. Finally, main advantages and disadvantages of our GPGA as well as performance comparison with sequential GA are stated.

关键词： parallel genetic algorithm multithreading tournament selection

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Implementation of parallel genetic algorithm Based on CUDA

Implementation of Parallel Genetic Algorithm Based on CUDA

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4th International Symposium on Intelligence Computation and Applications

作者： Zhang, Sifa He, Zhenming

ISBN: (纸本)9783642048425

genetic algorithm (GA) is a powerful tool for science computing, while parallel genetic algorithm (PGA) further promotes the performance of computing. However, the traditional parallel computing environment is very difficult to set up, much less the price. This gives rise to the appearance of moving dense computing to graphics hardware, which is inexpensive and more powerful. The paper presents a hierarchical parallel genetic algorithm, implemented by NVIDIA's Compute Unified Device Architecture (CUDA). Mixed with master-slave parallelization method and multiple-demes parallelization method, this algorithm has contributed to better utilization of threads and highspeed shared memory in CUDA.

关键词： genetic algorithm parallel genetic algorithm CUDA

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Research on Intelligent Generating Test Paper Based on parallel genetic algorithm

Research on Intelligent Generating Test Paper Based on Paral...

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Pacific-Asia Conference on Knowledge Engineering and Software Engineering

作者： Li, Jianjun Wang, Meng Guangxi Univ Technol Lushan Coll Liuzhou Peoples R China Guangxi Univ Technol Dept Comp Engn Liuzhou Peoples R China

ISBN: (纸本)9783642253485;9783642253492

The aim of test paper composing is to compose an optimization test paper that satisfies the parameters which the user inputs, so the test paper composing problem is a classical multi-objective linear programming problem. This paper proposes an intelligent algorithm to generating test paper based on parallel genetic algorithm, and provides a set of schemes of making papers of different degree of difficulties display in normal distribution. The algorithm adopts a new decimal system of subsection code, improves the traditional method of initializing the population and optimizes course of search. The experiment proves that this algorithm has better performance thus is more practical.

关键词： parallel genetic algorithm artificial intelligence Intelligent Generating Paper

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Model Development for Lattice Properties of Gallium Arsenide using parallel genetic algorithm

Model Development for Lattice Properties of Gallium Arsenide...

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IEEE Congress on Evolutionary Computation (CEC)

作者： Salmani-Jelodar, Mehdi Steiger, Sebastian Paul, Abhijeet Klimeck, Gerhard Purdue Univ Sch Elect & Comp Engn Network Computat Nanotechnol W Lafayette IN 47906 USA

ISBN: (纸本)9781424478354

In the last few years, evolutionary computing (EC) approaches have been successfully used for many real world optimization applications in scientific and engineering areas. One of these areas is computational nanoscience. Semi-empirical models with physics-based symmetries and properties can be developed by using EC to reproduce theoretically the experimental data. One of these semi-empirical models is the Valence Force Field (VFF) method for lattice properties. An accurate understanding of lattice properties provides a stepping stone for the investigation of thermal phenomena and has large impact in thermoelectricity and nano-scale electronic device design. The VFF method allows for the calculation of static properties like the elastic constants as well as dynamic properties like the sound velocity and the phonon dispersion. In this paper a parallel genetic algorithm (PGA) is employed to develop the optimal VFF model parameters for gallium arsenide (GaAs). This methodology can also be used for other semiconductors. The achieved results agree qualitatively and quantitatively with the experimental data.

关键词： gallium arsenide GaAs phonon dispersion elastic constants sound velocity parallel genetic algorithm valence force field model

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