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1 – 10 of over 18000
Article
Publication date: 21 November 2008

Stefania Siozou, Nikolaos Tselios and Vassilis Komis

The purpose of this paper is to compare the effect of different representations while teaching basic algorithmic concepts to novice programmers.

Abstract

Purpose

The purpose of this paper is to compare the effect of different representations while teaching basic algorithmic concepts to novice programmers.

Design/methodology/approach

A learning activity was designed and mediated with two conceptually different learning environments, each one used by a different group. The first group used the learning environment “Visual Flowchart”, which enables the students to construct and examine an algorithm using visual representation based on actual flowchart objects. The second group used the software “Language Interpreter”, which allows the students to express an algorithms using pseudocode.

Findings

Analysis of results among the two groups showed no statistically significant differences in the students’ performance with respect to the tool they used to solve the activity, the school stream they followed in high school and their gender.

Research limitations/implications

The lack of difference among the two groups could be attributed to the non‐complicated nature of the given activity. In addition, longitudinal studies of the effect of the different representation in the frame of an introductory first semester academic course in computer science could further validate the results.

Practical implications

Two alternative learning environments aimed to support learning of basic programming skills.

Originality/value

Two alternative learning environments were presented and discussed in detail, aimed to support learning of basic programming skills. The conclusions of the present study are in contrast to the research that has taken place in the past which compared usage of flowcharts and pseudocode to educate novice programmers, and wider adoption of “flowcharts” was depicted.

Details

Interactive Technology and Smart Education, vol. 5 no. 4
Type: Research Article
ISSN: 1741-5659

Keywords

Book part
Publication date: 1 January 2005

Hai Yang and Hai-Jun Huang

Abstract

Details

Mathematical and Economic Theory of Road Pricing
Type: Book
ISBN: 978-0-08-045671-3

Article
Publication date: 22 March 2013

Seyed Hossein Razavi Hajiagha, Hannan Amoozad Mahdiraji and Shide Sadat Hashemi

The purpose of this paper is to extend a methodology for solving multi‐objective linear programming (MOLP) problems, when the objective functions and constraints coefficients are…

Abstract

Purpose

The purpose of this paper is to extend a methodology for solving multi‐objective linear programming (MOLP) problems, when the objective functions and constraints coefficients are stated as interval numbers.

Design/methodology/approach

The approach proposed in this paper for the considered problem is based on the maximization of the sum of membership degrees which are defined for each objective of multi objective problem. These membership degrees are constructed based on the deviation from optimal solutions of individual objectives. Then, the final model based on membership degrees is itself an interval linear programming which can be solved by current methods.

Findings

The efficiency of the solutions obtained by the proposed method is proved. It is shown that the obtained solution by the proposed method for an interval multi objective problem is Pareto optimal.

Research limitations/implications

The proposed method can be used in modeling and analyzing of uncertain systems which are modeled in the context of multi objective problems and in which required information is ill defined.

Originality/value

The paper proposed a novel and well‐defined algorithm to solve the considered problem.

Article
Publication date: 5 April 2011

Amir Hossein Alavi and Amir Hossein Gandomi

The complexity of analysis of geotechnical behavior is due to multivariable dependencies of soil and rock responses. In order to cope with this complex behavior, traditional forms…

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Abstract

Purpose

The complexity of analysis of geotechnical behavior is due to multivariable dependencies of soil and rock responses. In order to cope with this complex behavior, traditional forms of engineering design solutions are reasonably simplified. Incorporating simplifying assumptions into the development of the traditional models may lead to very large errors. The purpose of this paper is to illustrate capabilities of promising variants of genetic programming (GP), namely linear genetic programming (LGP), gene expression programming (GEP), and multi‐expression programming (MEP) by applying them to the formulation of several complex geotechnical engineering problems.

Design/methodology/approach

LGP, GEP, and MEP are new variants of GP that make a clear distinction between the genotype and the phenotype of an individual. Compared with the traditional GP, the LGP, GEP, and MEP techniques are more compatible with computer architectures. This results in a significant speedup in their execution. These methods have a great ability to directly capture the knowledge contained in the experimental data without making assumptions about the underlying rules governing the system. This is one of their major advantages over most of the traditional constitutive modeling methods.

Findings

In order to demonstrate the simulation capabilities of LGP, GEP, and MEP, they were applied to the prediction of: relative crest settlement of concrete‐faced rockfill dams; slope stability; settlement around tunnels; and soil liquefaction. The results are compared with those obtained by other models presented in the literature and found to be more accurate. LGP has the best overall behavior for the analysis of the considered problems in comparison with GEP and MEP. The simple and straightforward constitutive models developed using LGP, GEP and MEP provide valuable analysis tools accessible to practicing engineers.

Originality/value

The LGP, GEP, and MEP approaches overcome the shortcomings of different methods previously presented in the literature for the analysis of geotechnical engineering systems. Contrary to artificial neural networks and many other soft computing tools, LGP, GEP, and MEP provide prediction equations that can readily be used for routine design practice. The constitutive models derived using these methods can efficiently be incorporated into the finite element or finite difference analyses as material models. They may also be used as a quick check on solutions developed by more time consuming and in‐depth deterministic analyses.

Details

Engineering Computations, vol. 28 no. 3
Type: Research Article
ISSN: 0264-4401

Keywords

Article
Publication date: 12 October 2012

Mingang Gao, Hong Chi, Baoguang Xu and Ruo Ding

The purpose of this paper is to focus on disruption management responding to large‐area flight delays (LFD). It is urgent for airways to reschedule the disrupted flights so as to…

1351

Abstract

Purpose

The purpose of this paper is to focus on disruption management responding to large‐area flight delays (LFD). It is urgent for airways to reschedule the disrupted flights so as to relieve the negative influence and minimize losses. The authors try to reduce the risk of airline company's credit and economic losses by rescheduling flights with mathematic models and algorithm.

Design/methodology/approach

Based on flight classifications of real‐time statuses and priority indicators, all flights are prioritized. In this paper, two mathematic programming models of flight rescheduling are proposed. For the second model, an optimum polynomial algorithm is designed.

Findings

In practice, when LFD happens, it is very important for the airline company to pay attention to real‐time statuses of all the flights. At the same time, the disruption management should consider not only the economic loss but also other non‐quantitative loss such as passengers' satisfaction, etc.

Originality/value

In this paper, two mathematic programming models of flight rescheduling are built. An algorithm is designed and it is proved to be an optimum polynomial algorithm and a case study is given to illustrate the algorithm. The paper provides a theory support for airways to reduce the risk brought by LFD.

Article
Publication date: 1 October 2005

Ralf Östermark

To solve the multi‐period portfolio management problem under transactions costs.

1650

Abstract

Purpose

To solve the multi‐period portfolio management problem under transactions costs.

Design/methodology/approach

We apply a recently designed super genetic hybrid algorithm (SuperGHA) – an integrated optimisation system for simultaneous parametric search and non‐linear optimisation – to a recursive portfolio management decision support system (SHAREX). The parametric search machine is implemented as a genetic superstructure, producing tentative parameter vectors that control the ultimate optimisation process.

Findings

SHAREX seems to outperform the buy and hold‐strategy on the Finnish stock market. The potential of a technical portfolio system is best exploitable under favorable market conditions.

Originality/value

A number of robust engines for matrix algebra, mathematical programming and numerical calculus have been integrated with SuperGHA. The engines expand its scope as a general‐purpose algorithm for mathematical programming.

Details

Kybernetes, vol. 34 no. 9/10
Type: Research Article
ISSN: 0368-492X

Keywords

Article
Publication date: 10 April 2009

O. Bozorg Haddad, A. Afshar and M.A. Mariño

The purpose of this paper is to present the honey‐bee mating optimization (HBMO) algorithm tested with, first, a well‐known, non‐linear, non‐separable, irregular, multi‐modal…

Abstract

Purpose

The purpose of this paper is to present the honey‐bee mating optimization (HBMO) algorithm tested with, first, a well‐known, non‐linear, non‐separable, irregular, multi‐modal “Fletcher‐Powell” function; and second, with a single hydropower reservoir operation optimization problem, to demonstrate the efficiency of the algorithm in handling complex mathematical problems as well as non‐convex water resource management problems. HBMO and genetic algorithm (GA) are applied to the second problem and the results are compared with those of a gradient‐based method non‐linear programming (NLP).

Design/methodology/approach

The HBMO algorithm is a hybrid optimization algorithm comprised of three features: simulated annealing, GA, and local search. This algorithm uses the individual features of these approaches and combines them together, resulting in an enhanced performance of HBMO in finding near optimal solutions.

Findings

Results of the “Fletcher‐Powell” function show more accuracy and higher convergence speed when applying HBMO algorithm rather than GA. When solving the single hydropower reservoir operation optimization problem, by disregarding evaporation from the model structure, both NLP solver and HBMO resulted in approximately the same near‐optimal solutions. However, when evaporation was added to the model, the NLP solver failed to find a feasible solution, whereas the proposed HBMO algorithm resulted in a feasible, near‐optimal solution.

Originality/value

This paper shows that the HBMO algorithm is not complicated to use and does not require much mathematical sophistication to understand its mechanisms. A tool such as the HBMO algorithm can be considered as an optimization tool able to provide alternative solutions from which designers/decision makers may choose.

Details

Engineering Computations, vol. 26 no. 3
Type: Research Article
ISSN: 0264-4401

Keywords

Article
Publication date: 15 June 2010

Zhendong Liu, Hengwu Li and Daming Zhu

The purpose of this paper is to design an algorithm to predict RNA secondary structure, compared with other relevant algorithm, its time complexity and space complexity are…

Abstract

Purpose

The purpose of this paper is to design an algorithm to predict RNA secondary structure, compared with other relevant algorithm, its time complexity and space complexity are reduced.

Design/methodology/approach

The dynamic programming algorithm need more time and space; it is very difficult to predict the RNA secondary structure which have more 1,000 bases. The nested RNA secondary structure algorithms cannot predict the RNA secondary structure containing pseudoknots, so the fast algorithm is needed to predict the RNA secondary structure containing pseudoknots urgently. Based on the greedy principle, a model is designed to solve the problem.

Findings

A greedy algorithm is presented to predict RNA secondary structure.

Research limitations/implications

The problem for predicting RNA secondary structure including pseudoknots is NP‐complete.

Practical implications

The paper presents a valuable and useful method for predicting the RNA secondary structure.

Originality/value

The new algorithm needs O(n3) time and O(n) space; the experimental results indicate that the algorithm has good accuracy and sensitivity.

Details

Kybernetes, vol. 39 no. 6
Type: Research Article
ISSN: 0368-492X

Keywords

Article
Publication date: 1 September 2004

Paul F. Whelan and Robert Sadleir

This paper details a free image analysis and software development environment for machine vision application development. The environment provides high‐level access to over 300…

Abstract

This paper details a free image analysis and software development environment for machine vision application development. The environment provides high‐level access to over 300 image manipulation, processing and analysis algorithms through a well‐defined and easy to use graphical interface. Users can extend the core library using the developer's interface via a plug‐in which features automatic source code generation, compilation with full error feedback and dynamic algorithm updates. Also discusses key issues associated with the environment and outline the advantages in adopting such a system for machine vision application development.

Details

Sensor Review, vol. 24 no. 3
Type: Research Article
ISSN: 0260-2288

Keywords

Article
Publication date: 16 March 2010

Chinho Lin and Ming‐Lung Hsu

The purpose of this paper is to provide an integrated group decision support system (GDSS) that will select the appropriate human resource (HR) capabilities for a firm by using…

2349

Abstract

Purpose

The purpose of this paper is to provide an integrated group decision support system (GDSS) that will select the appropriate human resource (HR) capabilities for a firm by using existing decision algorithms and information technology (IT) software systems.

Design/methodology/approach

The proposed GDSS is constructed by taking advantage of the characteristics of some existing analytical and mathematical methods, including electronic focus groups, value chain, HR scorecard, synergy analysis, gap analysis, analytic hierarchy process based on genetic algorithms (GA‐AHP), similarity measures, fuzzy set theory, and fuzzy mathematics programming. A case study is performed to test and evaluate the performance and usability of the GDSS and to identify whether or not it achieved its designed purpose.

Findings

The results show that the proposed GDSS can create a flexible and user‐friendly environment that aids managers and other relevant staff members in evaluating all relevant factors in selecting a firm's HR capabilities.

Practical implications

HR capabilities have a significant effect on business performance in the long term. However, not every firm can easily develop suitable HR capability strategies due to lacking of the adapted support tool. The proposed GDSS is proposed to provide a complete procedure to support managers using a strategy‐oriented perspective to decide the right HR capability to be developed. As the result of using the proposed GDSS, tasks are simplified and the time for HR capability analysis can be significantly reduced.

Originality/value

Few studies have discussed the application of IT to the selection of HR capabilities in facilitating managers in the strategic formulation process. This paper particularly focuses on the question of how firms can actually identify HR capabilities. Thus, the model‐developing nature‐oriented support system is provided for managers in solving such decision‐making problems.

Details

Industrial Management & Data Systems, vol. 110 no. 2
Type: Research Article
ISSN: 0263-5577

Keywords

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