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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.

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.

Sociological studies of entrepreneurship focus on social and technical innovations in business. Using an illustration from molecular plant biology and the historical evolution of the term “entrepreneur,” I make a case for the theoretical and methodological importance of studying entrepreneurs and their ventures outside the scope of traditional business. Then, considering the scientific lab as a self-consciously entrepreneurial venture, I use the population of molecular biology labs studying the plant Arabidopsis thaliana to demonstrate a relationship less directly measurable among start-ups in business: diverse sources of funding accompany original activities and ideas within a venture. This is not, however, what predicts lab success. Lab size drives success, but hinders originality. Moreover, I show that established institutions in science are usually the ones that become innovations in business.

X-Men is a movie franchise spanning 11 films centered on monsters and mutants (Braidotti, 1996), that is, the superheroes that appeared in the Marvel comics (Lauren Shuler Donner, 2000–2017). The franchise includes a rich compendium of male and female characters. Characters from both gender categories are gifted with powers and enjoy a remarkable focus from the plot. However, there are fewer female characters than male, and the former's powers are mainly related to the mind, rather than physical strength. If it is possible to immediately criticise the above-mentioned male focus, or the unequal distribution of powers, at the same time it is impossible to deny that both gender categories – male and female – reintroduce the gender binary that structures everyday reality in our current society (Butler, 2015). Such binary is a structural part of the cisgender and heteronormative system, inside which human beings carry out their existence. For these reasons, X-Men was interpreted by many transgender movements as a possible monstrous reclamation because it confers visibility to those bodies which are outside the norm (Preciado, 2020b) and it includes them in the context of a possible recognition as part of the cultural imaginary. This analysis, therefore, glimpses a possible liberation from the epistemological and material violence of the cisgender norm. This chapter will focus on the way in which the X-Men saga isn't faithful to a revolutionarily monstrous possibility, but rather carries out, through an apparatus of capture (Deleuze & Guattari, 2009), the reenactment of cis- and heteronormativity. In fact, those mutant and monstrous bodies represented here can be part of a highly popular franchise because they are part of the cisgender and heterosexual norm (Wittig, 1992) and because they put their monstrosity not outside the devices of power (Foucault, 2015), but at their service.

This paper aims to develop a new differential evolution algorithm (DEA) for solving the simple assembly line balancing problem of type 2 (SALBP-2).

Novel approaches of mutation operator and crossover operator are presented. A self-adaptive double mutation scheme is implemented and an elitist strategy is used in the selection operator.

Test and comparison results show that the proposed IDEA obtains better results for SALBP-2.

The presented DEA is called the integer-coded differential evolution algorithm (IDEA), which can directly deal with integer variables of SALBP-2 on a discrete space without any posterior conversion. The proposed IDEA will be an alternative in evolutionary algorithms, especially for various integer/discrete-valued optimization problems.

The image of Mel Gibson and Hugh Jackman (as Wolverine) in the Mad Max and X-Men franchises represents traditional heroic action masculinity. This chapter explores the roles of female action heroes in defying patriarchy and subverting action film genre stereotypes in male-dominated franchises. In contrast to past characterisations of Max, Mad Max: Fury Road (2015) provides both a departure to the role of Max as the male saviour seeking vengeance, by focusing on Imperator Furiosa and offering space for a portrayal of femininity characterised by inclusivity and tolerance. In Logan (2017), the decay of Wolverine is central to the narrative. Rather than the portrayal of an immortal hypermasculine hero in the previous X-Men films (with emphasis on men in X-Men), a new female mutant Laura assumes his mantle. In this context, I consider the gender roles and depiction of women in these films, and how they may be read as offering a futuristic vision of utopia in dystopian narrative worlds.

In the distant future, the social and economic systems build by the patriarchy are crumbling, causing an environmental crisis and divisive society, where people who are different (mutants) are hunted down. Mad Max: Fury Road and Logan both offer an alternative depiction of women and girls, providing new perspectives to navigate an uncertain dystopian world through fierce female warriors Furiosa, and mutant girl Laura. Ultimately this chapter demonstrates that survival in the post-apocalyptic and dystopian worlds represented in Mad Max: Fury Road and Logan may be achieved via a subversive feminist solution/utopia to the crisis of masculinity.

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.

Computational models can facilitate the understanding of complex biomedical systems such as in HIV/AIDS. Untangling the dynamics between HIV and CD4+ cellular populations and molecular interactions can be used to investigate the effective points of interventions in the HIV life cycle. With that in mind, we have developed a state transition systems dynamics and stochastic model that can be used to examine various alternatives for the control and treatment of HIV/AIDS. The specific objectives of our study were to use a cellular/molecular model to study optimal chemotherapies for reducing the HIV viral load and to use the model to study the pattern of mutant viral populations and resistance to drug therapies. The model considers major state variables (uninfected CD4+ lymphocytes, infected CD4+ cells, replicated virions) along with their respective state transition rates (viz. CD4+ replacement rate, infection rate, replication rate, depletion rate). The state transitions are represented by ordinary differential equations. The systems dynamics model was used for a variety of computational experimentations to evaluate HIV mutations, and to evaluate effective strategies in HIV drug therapy interventions.