Reversals are operations of great biological significance for the analysis of the evolutionary distance between organisms. Genome rearrangement through reversals, consists in finding the shortest sequence of reversals...
详细信息
Reversals are operations of great biological significance for the analysis of the evolutionary distance between organisms. Genome rearrangement through reversals, consists in finding the shortest sequence of reversals to transform one genome represented as a signed or unsigned permutation into another. When genes are non oriented and correspondingly permutations are unsigned, sorting by reversals came arise as a challenging problem in combinatorics of permutations. In fact, this problem is known to be NP-hard, but the question whether it is NP-complete remains open for more than twenty years. When permutations are signed and correspondingly genes are oriented, the problem is known to be in P. A parallelization of a standard GA (genetic Algorithm) is proposed for the problem of sorting unsigned permutations. This GA was previously reported in the literature as the most competitive regarding precision for which as control mechanism an 1.5-approximation algorithm was used. For the parallelization, the MPI Library of the C language was used and experiments were performed for calculating the execution time and precision. By increasing the number of individuals, experiment showed improvement in relation to previous approaches. Additionally, a virtualization of the GA using a MicroBlaze processor from Xilinx was performed on OVP for which the average number of executed instructions was of approximately 1.40 Giga instruction per second. In this extended version of this works originally presented in NaBIC 2013 biological data was generated and it was shown how the parallelization can be applied for their analysis. Specifically, the evolutionary distances between different pairs of organism were computed based on the set of non common genes in their mitochondrial DNA genome and the reversal distance between the sequences of common genes.
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. Sophistic...
详细信息
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 geneticalgorithms 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.
The performance of Grid applications may be very unstable, especially when using workflows for job distribution. This is mainly due to the Grid overheads, like scheduling and queuing, introduced before the job is exec...
详细信息
ISBN:
(纸本)9783662438800;9783662438794
The performance of Grid applications may be very unstable, especially when using workflows for job distribution. This is mainly due to the Grid overheads, like scheduling and queuing, introduced before the job is executed on a worker node. Optimization problems using geneticalgorithms (GAs) can be easily and efficiently implemented on Grids using Grid workflows. Due to the file dependencies introduced in the Grid workflows for GAs, mainly for genetic material interchange, these overheads are cumulative and thus very noticeable. This problem is also very evident when the jobs are short compared to the Grid overheads, i.e. the job spends more time waiting in a queue to be executed than the execution itself. In this paper we introduce a framework that enables users to easily utilize the Grid infrastructure for their optimization using GAs. It allows a user to preallocate certain number of pilot jobs, and also to dynamically manage their number for optimal availability of resources during the optimization process. In this way, once an application starts to execute the workloads, it will have at least one available pilot for execution of pooled tasks. This introduces better utilization of the Grid resources, as well boost the confidence in the infrastructure from users point of view.
Graph Coloring Problems (GCPs) are constraint optimization problems with various applications including time tabling and frequency allocation. The GCP consists in finding the minimum number of colors for coloring the ...
详细信息
Graph Coloring Problems (GCPs) are constraint optimization problems with various applications including time tabling and frequency allocation. The GCP consists in finding the minimum number of colors for coloring the graph vertices such that adjacent vertices have distinct colors. We propose a hierarchical approach based on parallel genetic algorithms (PGAs) to solve the GCP. We call this new approach Hierarchical PGAs (HPGAs). In addition, we have developed a new operator designed to improve PGAs when solving constraint optimization problems in general and GCPs in particular. We call this new operator genetic Modification (GM). Using the properties of variables and their relations, GM generates good individuals at each iteration and inserts them into the PGA population in the hope of reaching the optimal solution sooner. In the case of the GCP, the GM operator is based on a novel Variable Ordering Algorithm (VOA) that we propose. Together with the new crossover and the estimator of the initial solution we have developed, GM allows our solving approach to converge towards the optimal solution sooner than the well known methods for solving the GCP, even for hard instances. This was indeed clearly demonstrated by the experiments we conducted on the GCP instances taken from the well known DIMACS website.
In this paper, a parallelgenetic based association rule mining method is proposed to discover interesting rules from a large biological database. Apriori algorithms and its variants for association rule mining rely o...
详细信息
ISBN:
(纸本)9781467345286
In this paper, a parallelgenetic based association rule mining method is proposed to discover interesting rules from a large biological database. Apriori algorithms and its variants for association rule mining rely on two user specified threshold parameters such as minimum support and minimum confidence which is obviously an issue to be resolved. In addition, there are other issues like large search space and local optimality attracts many researchers to use heuristic mechanism. In the presence of large biological databases and with an aim to circumvent these problems, genetic algorithm may be taken as a suitable tool, but its computational cost is the main bottle-neck. Therefore, we choose parallel genetic algorithms to get relief from the pain of computational cost. The experimental result is promising and encouraging to do further research especially in the domain of biological science.
The topological features of the communication network between computing nodes in parallel genetic algorithms, under the framework of the island model, is discussed in the context of both the local rate of information ...
详细信息
ISBN:
(纸本)9783642386817
The topological features of the communication network between computing nodes in parallel genetic algorithms, under the framework of the island model, is discussed in the context of both the local rate of information exchange between nodes, and the global exchange rate that measures the level of information flow in the entire network. For optimal performance of parallelgenetic algorithm for a set of benchmark functions, the connectivity of the network can be found, corresponding to a global information exchange rate between 40-70%. This range is obtained by statistical analysis on the search for solutions of four benchmark problems: the 0-1 knapsack, the Weierstrass's function, the Ackley's function, and the Modified Shekel's foxholes function. Our method is based on the cutting of links of a fully connected network to gradually decrease the connectivity, and compare the performance of the genetic algorithm on each network. Suggestions for the protocol in applying this general guideline in the design of a good communication network for parallel genetic algorithms are made, where the islands are connected with 40% of links of a fully connected network before fine tuning the parameters of the island model to enhance performance in a specific problem.
In this paper, we develop a design methodology for information granulation-based genetically optimized fuzzy inference system, which deals with the tuning method with a variant identification ratio for structural as w...
详细信息
In this paper, we develop a design methodology for information granulation-based genetically optimized fuzzy inference system, which deals with the tuning method with a variant identification ratio for structural as well as parametric optimization of the reasoning system. The tuning is carried out with the aid of the hierarchical fair competition-based parallel genetic algorithms and it employs the mechanism of information granulation. This version of the genetic algorithm is a multi-population variant of parallel genetic algorithms, which is particularly suitable for handling multimodal problems of high-dimensionality. The granulation of information is realized with the aid of the C-Means clustering algorithm. The concept of information granulation is applied to the formation of the fuzzy inference system in order to realize its structural optimization. Here we divide the input space in order to construct the premise part of the fuzzy rules. Subsequently the consequence part of each fuzzy rule is organized based on the center points (prototypes) of data group obtained as a result of clustering. In particular, this concerns the fuzzy inference system-related parameters, i.e., the number of input variables to be used in the fuzzy inference system, a collection of a specific subset of input variables. the number of membership functions used for each input variable, and the polynomial type (order) occurring at the consequence part of fuzzy rules. Making use of a mechanism of simultaneous tuning for the parameters, we construct an optimized fuzzy inference system related to its structural as well as parametric optimization. A comparative analysis demonstrates that the proposed methodology leads to improved results when compared with some conventional methods exploited in fuzzy modeling. (C) 2008 Elsevier Inc. All rights reserved.
Mathematical models of pressure transients accompanied with cavitation and gas bubbles are studied in this paper to describe the flow behavior in a hydraulic pipeline. The reasonable prediction for pressure transients...
详细信息
Mathematical models of pressure transients accompanied with cavitation and gas bubbles are studied in this paper to describe the flow behavior in a hydraulic pipeline. The reasonable prediction for pressure transients in a low pressure hydraulic pipeline largely depends on several unknown parameters involved in the mathematical models, including the initial gas bubble volumes in hydraulic oils, gas releasing and resolving time constants. In order to identify the parameters in the mathematical models and to shorten the computation time of the identification, a new method-parallelgenetic algorithm (PGA)-is applied in this paper. Based on the least-square errors between the experimental data and simulation results, the fitness function of parallel genetic algorithms is programed and implemented. The global optimal parameters for hydraulic pipeline pressure transient models are obtained. The computation time of parallel genetic algorithms is much shorter than that of serial geneticalgorithms. By using PGAs, the executing time is 20 h. However, it takes about 204 h by using GAs. Simulation results with identified parameters obtained by parallel genetic algorithms agree well with the experimental data. The comparison between simulation results and the experimental data indicates that parallel genetic algorithms are feasible and efficient to estimate the unknown parameters in hydraulic pipeline transient models accompanied with cavitation and gas bubbles.
In this paper we investigate the applicability of geneticalgorithms (GAs) for solving Constraint Satisfaction Problems (CSPs). Despite some success of GAs when tackling CSPs, they generally suffer from poor crossover...
详细信息
ISBN:
(数字)9783642371981
ISBN:
(纸本)9783642371981
In this paper we investigate the applicability of geneticalgorithms (GAs) for solving Constraint Satisfaction Problems (CSPs). Despite some success of GAs when tackling CSPs, they generally suffer from poor crossover operators. In order to overcome this limitation in practice, we propose a novel crossover specifically designed for solving CSPs. Together with a variable ordering heuristic and an integration into a parallel architecture, this proposed crossover enables the solving of large and hard problem instances as demonstrated by the experimental tests conducted on randomly generated CSPs based on the model RB. We will indeed demonstrate, through these tests, that our proposed method is superior to the known GA based techniques for CSPs. In addition, we will show that we are able to compete with the efficient MAC-based Abscon 109 solver for random problem instances.
Optimization and solving NP-hard problem are very important and Evolutionary Computing methods are useful and popular. There are different types of EC methods that most of them are serial and some other one has parall...
详细信息
ISBN:
(纸本)9781467364904;9781467364898
Optimization and solving NP-hard problem are very important and Evolutionary Computing methods are useful and popular. There are different types of EC methods that most of them are serial and some other one has parallel implementation. In first step we want review some parallel implementation of EC methods and in second step we introduce three general frame work to parallelize all serial EC methods.
暂无评论