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The purpose of this study is to reduce the number of mutations and, consequently, reduce the cost of mutation test. The results of related studies indicate that about 40% of injected faults (mutants) in the source code are effect-less (equivalent). Equivalent mutants are one of the major costs of mutation testing and the identification of equivalent and effect-less mutants has been known as an undecidable problem.

In a program with n branch instructions (if instruction) there are 2n execution paths (test paths) that the data and codes into each of these paths can be considered as a target of mutation. Given the role and impact of data in a program, some of data and codes propagates the injected mutants more likely to the output of the program. In this study, firstly the error-propagation rate of the program data is quantified using static analysis of the program control-flow graph. Then, the most error-propagating test paths are identified by the proposed heuristic algorithm (Genetic Algorithm [GA]). Data and codes with higher error-propagation rate are only considered as the strategic locations for the mutation testing.

In order to evaluate the proposed method, an extensive series of mutation testing experiments have been conducted on a set of traditional benchmark programs using MuJava tool set. The results depict that the proposed method reduces the number of mutants about 24%. Also, in the corresponding experiments, the mutation score is increased about 5.6%. The success rate of the GA in finding the most error-propagating paths of the input programs is 99%. On average, only 7.46% of generated mutants by the proposed method are equivalent. Indeed, 92.54% of generated mutants are non-equivalent.

The main contribution of this study is as follows: Proposing a set of equations to measure the error-propagation rate of each data, basic-block and execution path of a program. Proposing a genetic algorithm to identify a most error-propagating path of program as locations of mutations. Developing an efficient mutation-testing framework that mutates only the strategic locations of a program identified by the proposed genetic algorithms. Reducing the time and cost of mutation testing by reducing the equivalent mutants.

For delivering high-quality software applications, proper testing is required. A software test will function successfully if it can find more software faults. The traditional method of assessing the quality and effectiveness of a test suite is mutation testing. One of the main drawbacks of mutation testing is its computational cost. The research problem of this study is the high computational cost of the mutation test. Reducing the time and cost of the mutation test is the main goal of this study.

With regard to the 80–20 rule, 80% of the faults are found in 20% of the fault-prone code of a program. The proposed method statically analyzes the source code of the program to identify the fault-prone locations of the program. Identifying the fault-prone (complex) paths of a program is an NP-hard problem. In the proposed method, a firefly optimization algorithm is used for identifying the most fault-prone paths of a program; then, the mutation operators are injected only on the identified fault-prone instructions.

The source codes of five traditional benchmark programs were used for evaluating the effectiveness of the proposed method to reduce the mutant number. The proposed method was implemented in Matlab. The mutation injection operations were carried out by MuJava, and the output was investigated. The results confirm that the proposed method considerably reduces the number of mutants, and consequently, the cost of software mutation-test.

The proposed method avoids the mutation of nonfault-prone (simple) codes of the program, and consequently, the number of mutants considerably is reduced. In a program with n branch instructions (if instruction), there are 2n execution paths (test paths) that the data and codes into each of these paths can be considered as a target of mutation. Identifying the error-prone (complex) paths of a program is an NP-hard problem. In the proposed method, a firefly optimization algorithm as a heuristic algorithm is used for identifying the most error-prone paths of a program; then, the mutation operators (faults) are injected only on the identified fault-prone instructions.

Darwin’s theory of natural selection, which explains how individual organisms can become exquisitely adapted to their environments, does not explain the evolution of adaptive societies with equal ease. To understand the nature of the problem, imagine a mutant individual who behaves in a way that increases the survival of everyone in her society, including herself, to an equal degree. Such a “no-cost public good” might not appear very feasible (and will soon be amended), but is useful for illustrative purposes. By increasing the fitness of everyone, the mutant trait will not increase in frequency within the society (other than by drift, which can equally cause a decrease in frequency). This example illustrates the elementary fact that natural selection is based on relative fitness. It’s not enough for a mutant trait to increase its own survival and reproduction; it must do so more than alternative traits in the population. The relative nature of fitness makes the evolutionary forces within a population insensitive to the welfare of the population as a whole.

DDLab is interactive graphics software for creating and visualizing discrete dynamical networks, and studying their behavior in terms of both space‐time patterns and basins of attraction. The networks can range from cellular automata to random Boolean networks. This article provides some general background, and gives the flavor of DDLab with a range of examples. Further details can be found at www.ddlab.comwww.ddlab.com

Much to the relief of everyone, the general election has come and gone and with it the boring television drivel; the result a foregone conclusion. The Labour/Trade Union movement with a severe beating, the worst for half a century, a disaster they have certainly been asking for. Taking a line from the backwoods wisdom of Abraham Lincoln — “You can't fool all the people all the time!” Now, all that most people desire is not to live easy — life is never that and by the nature of things, it cannot be — but to have a reasonably settled, peaceful existence, to work out what they would consider to be their destiny; to be spared the attentions of the planners, the plotters, provocateurs, down to the wilful spoilers and wreckers. They have a right to expect Government protection. We cannot help recalling the memory of a brilliant Saturday, but one of the darkest days of the War, when the earth beneath our feet trembled at the destructive might of fleets of massive bombers overhead, the small silvery Messerschmits weaving above them. Believing all to be lost, we heaped curses on successive Governments which had wrangled over rearmament, especially the “Butter before Guns” brigade, who at the word conscription almost had apoplexy, and left its people exposed to destruction. Now, as then, the question is “Have they learned anything?” With all the countless millions Government costs, its people have the right to claim something for their money, not the least of which is the right to industrial and domestic peace.

Real-world applications in engineering and other fields usually involve simultaneous optimization of multiple objectives, which are generally non-commensurable and conflicting with each other. This paper aims to treat the transformer design optimization (TDO) as a multiobjective problem (MOP), to minimize the manufacturing cost and the total owing cost, taking into consideration design constraints.

To deal with this optimization problem, a new method is proposed that combines the unrestricted population-size evolutionary multiobjective optimization algorithm (UPS-EMOA) with differential evolution, also applying lognormal distribution for tuning the scale factor and the beta distribution to adjust the crossover rate (UPS-DELFBC). The proposed UPS-DELFBC is useful to maintain the adequate diversity in the population and avoid the premature convergence during the generational cycle. Numerical results using UPS-DELFBC applied to the transform design optimization of 160, 400 and 630 kVA are promising in terms of spacing and convergence criteria.

Numerical results using UPS-DELFBC applied to the transform design optimization of 160, 400 and 630 kVA are promising in terms of spacing and convergence criteria.

This paper develops a promising UPS-DELFBC approach to solve MOPs. The TDO problems for three different transformer specifications, with 160, 400 and 630 kVA, have been addressed in this paper. Optimization results show the potential and efficiency of the UPS-DELFBC to solve multiobjective TDO and to produce multiple Pareto solutions.

This paper aims to investigate to what extent hybrid differential evolution (DE) algorithms can be successful in solving the optimal camera placement problem.

This problem is stated as a unicost set covering problem (USCP) and 18 problem instances are defined according to practical operational needs. Three methods are selected from the literature to solve these instances: a CPLEX solver, greedy algorithm and row weighting local search (RWLS). Then, it is proposed to hybridize these algorithms with two hybrid DE approaches designed for combinatorial optimization problems. The first one is a set-based approach (DEset) from the literature. The second one is a new similarity-based approach (DEsim) that takes advantage of the geometric characteristics of a camera to find better solutions.

The experimental study highlights that RWLS and DEsim-CPLEX are the best proposed algorithms. Both easily outperform CPLEX, and it turns out that RWLS performs better on one class of problem instances, whereas DEsim-CPLEX performs better on another class, depending on the minimal resolution needed in practice.

Up to now, the efficiency of RWLS and the DEset approach has been investigated only for a few problems. Thus, the first contribution is to apply these methods for the first time in the context of camera placement. Moreover, new hybrid DE algorithms are proposed to solve the optimal camera placement problem when stated as a USCP. The second main contribution is the design of the DEsim approach that uses the distance between camera locations to fully benefit from the DE mutation scheme.

Evolution has long been a biological process “borrowed” by management sciences to define structural and procedural development in organizations. The theory of Darwinian Evolution in biology has existed for a long time and still (with modification) remains the main theory in life sciences. However in biotechnology new concepts have risen. In parallel, organization sciences have been evolving the concept of evolution on different levels of the organization, discussing the evolution of organization during their life cycle, the evolution of populations of organizations, sectors, etc. Directed evolution in biology creates new organisms that can produce molecules with attributes better fitting industrial use, from naturally occurring organisms, allowing new organisms to function in non‐biological environments and perform processes they never needed to perform in a natural environment. We will show that by translating the concept from biology into organization sciences, we can develop the techniques for the evolution of new organizational structures and fitting routines, that would fit new emerging environments, where we seek the best adapted routines and structures for performance. We will adopt the concept of directly evolving a structure fitting for pre‐designed purposes by using bio‐technology methods, and will try and bridge the gap in organization sciences between the current development of the evolutionary theory and the advance made in biology. At the end discusses opportunities for research (the European Framework Program, national programs), together with a proposed general plan of action. The theory and the techniques descried can lead to further research and active experimentation.

Impedance data obtained by electrochemical impedance spectroscopy (EIS) are fitted to a relevant electrical equivalent circuit to evaluate parameters directly related to the resistance and the durability of metal–coating systems. The purpose of this study is to present a novel and more efficient computational strategy for the modelling of EIS measurements using the Differential Evolution paradigm.

An alternative method to non-linear regression algorithms for the analysis of measured data in terms of equivalent circuit parameters is provided by evolutionary algorithms, particularly the Differential Evolution (DE) algorithms (standard DE and a representative of the self-adaptive DE paradigm were used).

The results obtained with DE algorithms were compared with those yielding from commercial fitting software, achieving a more accurate solution, and a better parameter identification, in all the cases treated. Further, an enhanced fitting power for the modelling of metal–coating systems was obtained.

The great potential of the developed tool has been demonstrated in the analysis of the evolution of EIS spectra due to progressive degradation of metal–coating systems. Open codes of the different differential algorithms used are included, and also, examples tackled in the document are open. It allows the complete use, or improvement, of the developed tool by researchers.