Removal of defects is the key in ensuring long-term error free operation of a software system. Although improvements in the software testing process has resulted in better coverage, it is evident that some parts of a ...
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Removal of defects is the key in ensuring long-term error free operation of a software system. Although improvements in the software testing process has resulted in better coverage, it is evident that some parts of a software system tend to be more defect prone than the other parts and identification of these parts can greatly benefit the software practitioners in order to deliver high quality maintainable software products. A defect prediction model is built by training a learner using the software metrics. These models can later be used to predict defective classes in a software system. Many studies have been conducted in the past for predicting defective classes in the early phases of the software development. However, the evolutionary computation techniques have not yet been explored for predicting defective classes. The nature of evolutionary computation techniques makes them better suited to the software engineering problems. In this study we explore the predictive ability of the evolutionary computation and hybridized evolutionary computation techniques for defect prediction. This work contributes to the literature by examining the effectiveness of the 15 evolutionary computation and hybridized evolutionary computation techniques to 5 datasets obtained from the Apache Software Foundation using the Defect Collection and Reporting System. The results are evaluated in terms of the values of accuracy. We further compare the evolutionary computation techniques using the Friedman ranking. The results suggest that the defect prediction models built using the evolutionary computation techniques perform well over all the datasets in terms of prediction accuracy.
Early lifecycle software design is an intensely human activity in which design scale and complexity can place a high cognitive load on the software designer. Recently, the use of evolutionary search has been suggested...
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Early lifecycle software design is an intensely human activity in which design scale and complexity can place a high cognitive load on the software designer. Recently, the use of evolutionary search has been suggested to yield insights in the nature of software engineering problems generally, and so we have applied dynamic evolutionary computation using self-adaptive mutation to the object-oriented software design search space. Using three design problem instances of varying scale and complexity, initial investigations of the discrete search landscape reveal a redundancy in genotype-to-phenotype mapping enabling flexible and effective exploration. In further experiments, mutation probabilities and population diversity are observed to significantly increase in the face of increasing problem scale, but not for increasing complexity (in problems of the same scale). Based on these findings, we conclude that design problem scale rather than complexity has an effect on the software design process, emphasizing the role of decomposition as a design technique.
In this paper, an algorithm for many-objective evolutionary computation, which is based on the NSGA-II with the Chebyshev preference relation, is applied to multi-objective design optimization problem of dielectric ba...
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In this paper, an algorithm for many-objective evolutionary computation, which is based on the NSGA-II with the Chebyshev preference relation, is applied to multi-objective design optimization problem of dielectric barrier discharge plasma actuator (DBDPA). The present optimization problem has four design parameters and six objective functions. The main goal of the paper is to extract useful design guidelines to predict control flow behavior based on the DBDPA parameter values using the resulting approximation Pareto set obtained by the optimization.
In this work, we propose a hybrid AI system consisting of a multi-agent system simulating students during an exam and a teacher monitoring them, as well as an evolutionary algorithm that finds classroom arrangements w...
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ISBN:
(纸本)9783031741821;9783031741838
In this work, we propose a hybrid AI system consisting of a multi-agent system simulating students during an exam and a teacher monitoring them, as well as an evolutionary algorithm that finds classroom arrangements which minimize cheating incentives. The students will answer the exam based on how much knowledge they have about the topic of the exam. In our simulation, then they enter a decision phase in which, for those questions they don't know the answer to, they will either cheat or answer by guessing. If a student gets caught cheating, his/her exam will be cancelled. The purpose of this study is to examine the question of how different monitoring behaviors on the part of the teacher affect the cheating behaviors of students. The results of this study show that an unbiased teacher, that is, a teacher that monitors every student with the same probability, produces minimal cheating incentives for students.
Optimization is an old research topic, which widely exists in many engineering problems. Till now loads of methods have been proposed to handle complex optimization problems, amongst which evolutionary algorithms have...
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Optimization is an old research topic, which widely exists in many engineering problems. Till now loads of methods have been proposed to handle complex optimization problems, amongst which evolutionary algorithms have attracted a great deal of attentions due to their robustness to the underlying problem characteristics. However, evolutionary algorithms which simulate the evolution of nature is an iterative optimizer, causing high computational effort to approximate the optima. That is, such methods may not be applicable on online or real-time optimization.
In this paper, we describe a novel application of evolutionary computation, namely for evolving ontologies. The general algorithm of evolutionary ontologies follow roughly the same guidelines as any other genetic algo...
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ISBN:
(纸本)9781479914869
In this paper, we describe a novel application of evolutionary computation, namely for evolving ontologies. The general algorithm of evolutionary ontologies follow roughly the same guidelines as any other genetic algorithms. However, we introduced a new genetic operator, called repair, which is needed in order to make the offspring viable. Experiments for the generation of user centered automatically generated scenes demonstrate the performance of the proposed approach.
Type design is a field that deals with the creation of visually appealing designs for the written language. The work of the designer is time-consuming and requires many iterations until the final solution is achieved....
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ISBN:
(数字)9783031569920
ISBN:
(纸本)9783031569913;9783031569920
Type design is a field that deals with the creation of visually appealing designs for the written language. The work of the designer is time-consuming and requires many iterations until the final solution is achieved. Although a human expert is required to validate the final results, this task can be aided by automatic design software. We propose Evoboard, an automatic algorithm that evolves a typefont using a geoboard-inspired representation where each character is a self-intersecting polygon. Evoboard uses a genetic algorithm to optimize the number of vertices of the polygon and their positions in a grid. The evolution of the population is guided by an Optical Character Recognition (OCR) model that aims to maximize the recognition of the polygon as the target character. Thanks to this simple pipeline, both the OCR model and the representation can be easily modified by the user to their needs. We evolve a set of 36 alphanumeric characters that are both highly legible and aesthetically appealing, two important aspects of type design.
In agriculture, weeds reduce soil productivity and harvest quality. A common practice for weed control is via weed spraying. Ground spray of weeds is a common approach that may be harmful, destructive, and too slow, w...
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ISBN:
(数字)9783031590573
ISBN:
(纸本)9783031590566;9783031590573
In agriculture, weeds reduce soil productivity and harvest quality. A common practice for weed control is via weed spraying. Ground spray of weeds is a common approach that may be harmful, destructive, and too slow, while aerial UAV spraying can be safe, non-destructive, and quick. Spraying efficiency and accuracy can be enhanced when adopting multiple UAVs. In this context, we propose a new multiple UAV spraying system that autonomously and accurately sprays weeds within the field. In our proposed system, a weed pressure map is first clustered. Then, the voronoi approach generates the appropriate number of waypoints. Finally, a variant of the Traveling Salesman Problem (TSP) is solved to find the best UAV tour for each cluster. The latter task is performed using two nature-inspired techniques, namely, NSGA2 and MOEA/D. To assess the performance of each method, we conducted a set of simulation tests. The results reported in this paper demonstrate the superiority of NSGA2 over MOEA/D. In addition, the heterogeneity of UAVs is studied, where we have a mix of fixed-wing and multi-rotor drones for spraying.
The performance of a multimodal evolutionary algorithm is highly sensitive to the setting of population size. This paper introduces a generic archive technique to reduce the importance of properly setting the populati...
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ISBN:
(纸本)9781479944576
The performance of a multimodal evolutionary algorithm is highly sensitive to the setting of population size. This paper introduces a generic archive technique to reduce the importance of properly setting the population size parameter. The proposed archive technique contains two components: subpopulation identification and convergence detection. The first component is used to identify subpopulations in a number of individuals while the second one is used to determine whether a subpopulation is converged. By using the two components, converged subpopulations are identified, and then, individuals in the converged subpopulations are stored in an external archive and re-initialized to search for other optima. We integrate the archive technique with several state-of-the-art PSO-based multimodal algorithms. Experiments are carried out on a recently proposed multimodal problem set to investigate the effect of the archive technique. The experimental results show that the proposed method can reduce the influence of the population size parameter and improve the performance of multimodal algorithms.
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