Nawwaf N. Kharma

Research Note: A high performance algorithm for static task scheduling in heterogeneous distributed computing systems

Effective task scheduling is essential for obtaining high performance in heterogeneous distributed computing systems (HeDCSs). However, finding an effective task schedule in HeDCSs requires the consideration of both the heterogeneity of processors and high interprocessor communication overhead, which results from non-trivial data movement between tasks scheduled on different processors. In this paper, we present a new high-performance scheduling algorithm, called the longest dynamic critical path (LDCP) algorithm, for HeDCSs with a bounded number of processors. The LDCP algorithm is a list-based scheduling algorithm that uses a new attribute to efficiently select tasks for scheduling in HeDCSs. The efficient selection of tasks enables the LDCP algorithm to generate high-quality task schedules in a heterogeneous computing environment. The performance of the LDCP algorithm is compared to two of the best existing scheduling algorithms for HeDCSs: the HEFT and DLS algorithms. The comparison study shows that the LDCP algorithm outperforms the HEFT and DLS algorithms in terms of schedule length and speedup. Moreover, the improvement in performance obtained by the LDCP algorithm over the HEFT and DLS algorithms increases as the inter-task communication cost increases. Therefore, the LDCP algorithm provides a practical solution for scheduling parallel applications with high communication costs in HeDCSs.

Genetic algorithms for feature selection and weighting, a review and study

Our aim is: a) to present a comprehensive survey of previous attempts at using genetic algorithms (GA) for feature selection in pattern recognition applications, with a special focus on character recognition; and b) to report on work that uses GA to optimize the weights of the classification module of a character recognition system. The main purpose of feature selection is to reduce the number of features, by eliminating irrelevant and redundant features, while simultaneously maintaining or enhancing classification accuracy. Many search algorithms have been used for feature selection. Among those, GA have proven to be an effective computational method, especially in situations where the search space is uncharacterized (mathematically), not fully understood, or/and highly dimensional.

Bi-Objective Multipopulation Genetic Algorithm for Multimodal Function Optimization

This paper describes the latest version of a bi-objective multipopulation genetic algorithm (BMPGA) aiming to locate all global and local optima on a real-valued differentiable multimodal landscape. The performance of BMPGA is compared against four multimodal GAs on five multimodal functions. BMPGA is distinguished by its use of two separate but complementary fitness objectives designed to enhance the diversity of the overall population and exploration of the search space. This is coupled with a multipopulation and clustering scheme, which focuses selection within the various sub-populations and results in effective identification and retention of the optima of the target functions as well as improved exploitation within promising areas. The results of the empirical comparison provide clear evidence that supports the conclusion that BMPGA is better than the other GAs in terms of overall effectiveness, applicability, and reliability. The practical value of BMPGA has already been demonstrated in applications to multiple ellipses and elliptic objects detection in microscopic imagery.

A hybrid heuristic-genetic algorithm for task scheduling in heterogeneous processor networks

Efficient task scheduling on heterogeneous distributed computing systems (HeDCSs) requires the consideration of the heterogeneity of processors and the inter-processor communication. This paper presents a two-phase algorithm, called H2GS, for task scheduling on HeDCSs. The first phase implements a heuristic list-based algorithm, called LDCP, to generate a high quality schedule. In the second phase, the LDCP-generated schedule is injected into the initial population of a customized genetic algorithm, called GAS, which proceeds to evolve shorter schedules. GAS employs a simple genome composed of a two-dimensional chromosome. A mapping procedure is developed which maps every possible genome to a valid schedule. Moreover, GAS uses customized operators that are designed for the scheduling problem to enable an efficient stochastic search. The performance of each phase of H2GS is compared to two leading scheduling algorithms, and H2GS outperforms both algorithms. The improvement in performance obtained by H2GS increases as the inter-task communication cost increases.

A multi-population genetic algorithm for robust and fast ellipse detection

This paper discusses a novel and effective technique for extracting multiple ellipses from an image, using a genetic algorithm with multiple populations (MPGA). MPGA evolves a number of subpopulations in parallel, each of which is clustered around an actual or perceived ellipse in the target image. The technique uses both evolution and clustering to direct the search for ellipses—full or partial. MPGA is explained in detail, and compared with both the widely used randomized Hough transform (RHT) and the sharing genetic algorithm (SGA). In thorough and fair experimental tests, using both synthetic and real-world images, MPGA exhibits solid advantages over RHT and SGA in terms of accuracy of recognition—even in the presence of noise or/and multiple imperfect ellipses in an image—and speed of computation.

Advances in detecting parkinson's disease

Diagnosing disordered subjects is of considerable importance in medical biometrics. In this study, aimed to provide medical decision boundaries for detecting Parkinsons disease (PD), we combine genetic programming and the expectation maximization algorithm (GP-EM) to create learning feature functions on the basis of ordinary feature data (features of voice). Via EM, the transformed data are modeled as a Gaussians mixture, so that the learning processes with GP are evolved to fit the data into the modular structure, thus enabling the efficient observation of class boundaries to separate healthy subjects from those with PD. The experimental results show that the proposed biometric detector is comparable to other medical decision algorithms existing in the literature and demonstrates the effectiveness and computational efficiency of the mechanism.

Fast robust GA-based ellipse detection

This paper discusses a novel and effective technique for extracting multiple ellipses from an image, using a multi-population genetic algorithm (MPGA). MPGA evolves a number of subpopulations in parallel, each of which is clustered around an actual or perceived ellipse. It utilizes both evolution and clustering to direct the search for ellipses - full or partial. MPGA is explained in detail, and compared with both the widely used randomized Hough transform (RHT) and the sharing genetic algorithm (SGA). In thorough and fair experimental tests, utilizing both synthetic and real-world images, MPGA exhibits solid advantages over RHT and SGA in terms of accuracy of recognition - even in the presence of noise or/and multiple imperfect ellipses, as well as speed of computation.

Evolving novel image features using Genetic Programming-based image transforms

In this paper, we use Genetic Programming (GP) to define a set of transforms on the space of greyscale images. The motivation is to allow an evolutionary algorithm means of transforming a set of image patterns into a more classifiable form. To this end, we introduce the notion of a Transform-based Evolvable Feature (TEF), a moment value extracted from a GP-transformed image, used in a classification task. Unlike many previous approaches, the TEF allows the whole image space to be searched and augmented. TEFs are instantiated through Cartesian Genetic Programming, and applied to a medical image classification task, that of detecting muscular dystrophy-indicating inclusions in cell images. It is shown that the inclusion of a single TEF allows for significantly superior classification relative to predefined features alone.

Automated Coverage Directed Test Generation Using a Cell-Based Genetic Algorithm

Functional verification is a major challenge in the hardware design development cycle. Defining the appropriate coverage points that capture the designs functionalities is a non-trivial problem. However, the real bottleneck remains in generating the suitable testbenches that activate those coverage points adequately. In this paper, we propose an approach to enhance the coverage rate of multiple coverage points through the automatic generation of appropriate test patterns. We employ a directed random simulation, where directives are continuously updated until achieving acceptable coverage rates for all coverage points. We propose to model the solution of the test generation problem as sequences of directives or cells, each of them with specific width, height and distribution. Our approach is based on a genetic algorithm, which automatically optimizes the widths, heights and distributions of these cells over the whole input domain with the aim of enhancing the effectiveness of test generation. We illustrate the efficiency of our approach on a set of designs modeled in SystemC

Optimal powertrain component sizing of a fuel cell plug-in hybrid electric vehicle using multi-objective genetic algorithm

Considerable efforts have been made recently to develop a completely zero-emission and highly fuel efficient vehicle. Due to clean and efficient power generation, the hydrogen fed fuel cell vehicle (FCVs) has received considerable attention. However, major obstacles such as cost of the hydrogen infrastructure, driving range, and cost of the fuel cell greatly influence FCV development. At the same time, proper utilization of grid power, along with a modified electrical system infrastructure, would encourage automakers to envisage plug-in versions of fuel cell vehicles. This paper presents the optimal powertrain component sizing of a fuel cell plug-in hybrid electric (FC-PHEV) vehicle, comprised of a fuel cell with electrolyser, Ni-MH battery as secondary energy storage, and a propulsion motor. Such a PHEV architecture provides an additional degree of freedom, as the gird power can be used to recharge batteries, or for the electrolysis of water, to generate hydrogen and oxygen, which increases the driving range of vehicle as well as the overall powertrain efficiency. Hence, the overall performance and efficiency are much superior when compared to ordinary PHEV or FC-HEV powertrains. This paper uses a small vehicle power train for modelling and simulation purposes. Optimal sizing of the power train components using multi-objective genetic algorithm will be presented. Moreover, overall vehicle performance and fuel economy for different driving loads will also be analysed. Finally, an overall cost analysis will also be presented.