Maximiliano Cristia and Claudia Frydman
This paper aims to present the verification process conducted to assess the functional correctness of the voting system. Consejo Nacional de Investigaciones Científicas y Técnicas…
Abstract
Purpose
This paper aims to present the verification process conducted to assess the functional correctness of the voting system. Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET) is the most important research institution in Argentina. It depends directly from Argentina’s President but its internal authorities are elected by around 8,000 research across the country. During 2011, the CONICET developed a Web voting system to replace the traditional mail-based process. In 2012 and 2014, CONICET conducted two Web election with no complaints from candidates and voters. Before moving the system into production, CONICET asked the authors to conduct a functional and security assessment of it.
Design/methodology/approach
This process is the result of integrating formal, semi-formal and informal verification activities from formal proof to code inspection and model-based testing.
Findings
Given the resources and time available, a reasonable level of confidence on the correctness of the application could be transmitted to senior management.
Research limitations/implications
A formal specification of the requirements must be developed.
Originality/value
Formal methods and semi-formal activities are seldom applied to Web applications.
Details
Keywords
Areej Ahmad Alsaadi, Wadee Alhalabi and Elena-Niculina Dragoi
Differential search algorithm (DSA) is a new optimization, meta-heuristic algorithm. It simulates the Brownian-like, random-walk movement of an organism by migrating to a better…
Abstract
Purpose
Differential search algorithm (DSA) is a new optimization, meta-heuristic algorithm. It simulates the Brownian-like, random-walk movement of an organism by migrating to a better position. The purpose of this paper is to analyze the performance analysis of DSA into two key parts: six random number generators (RNGs) and Benchmark functions (BMF) from IEEE World Congress on Evolutionary Computation (CEC, 2015). Noting that this study took problem dimensionality and maximum function evaluation (MFE) into account, various configurations were executed to check the parameters’ influence. Shifted rotated Rastrigin’s functions provided the best outcomes for the majority of RNGs, and minimum dimensionality offered the best average. Among almost all BMFs studied, Weibull and Beta RNGs concluded with the best and worst averages, respectively. In sum, 50,000 MFE provided the best results with almost RNGs and BMFs.
Design/methodology/approach
DSA was tested under six randomizers (Bernoulli, Beta, Binomial, Chisquare, Rayleigh, Weibull), two unimodal functions (rotated high conditioned elliptic function, rotated cigar function), three simple multi-modal functions (shifted rotated Ackley’s, shifted rotated Rastrigin’s, shifted rotated Schwefel’s functions) and three hybrid Functions (Hybrid Function 1 (n=3), Hybrid Function 2 (n=4,and Hybrid Function 3 (n=5)) at four problem dimensionalities (10D, 30D, 50D and 100D). According to the protocol of the CEC (2015) testbed, the stopping criteria are the MFEs, which are set to 10,000, 50,000 and 100,000. All algorithms mentioned were implemented on PC running Windows 8.1, i5 CPU at 1.60 GHz, 2.29 GHz and a 64-bit operating system.
Findings
The authors concluded the results based on RNGs as follows: F3 gave the best average results with Bernoulli, whereas F4 resulted in the best outcomes with all other RNGs; minimum and maximum dimensionality offered the best and worst averages, respectively; and Bernoulli and Binomial RNGs retained the best and worst averages, respectively, when all other parameters were fixed. In addition, the authors’ results concluded, based on BMFs: Weibull and Beta RNGs produced the best and worst averages with most BMFs; shifted and rotated Rastrigin’s function and Hybrid Function 2 gave rise to the best and worst averages. In both parts, 50,000 MFEs offered the best average results with most RNGs and BMFs.
Originality/value
Being aware of the advantages and drawbacks of DS enlarges knowledge about the class in which differential evolution belongs. Application of that knowledge, to specific problems, ensures that the possible improvements are not randomly applied. Strengths and weaknesses influenced by the characteristics of the problem being solved (e.g. linearity, dimensionality) and by the internal approaches being used (e.g. stop criteria, parameter control settings, initialization procedure) are not studied in detail. In-depth study of performance under various conditions is a “must” if one desires to efficiently apply DS algorithms to help solve specific problems. In this work, all the functions were chosen from the 2015 IEEE World Congress on Evolutionary Computation (CEC, 2015).