A software defined networking(SDN) system has a logically centralized control plane that maintains a global network view and enables network-wide management, optimization, and innovation. Network-wide management and o...
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A software defined networking(SDN) system has a logically centralized control plane that maintains a global network view and enables network-wide management, optimization, and innovation. Network-wide management and optimization problems are typicallyvery complex with a huge solution space, large number of variables, and multiple objectives. Heuristic algorithms can solve theseproblems in an acceptable time but are usually limited to some particular problem circumstances. On the other hand, evolutionaryalgorithms(EAs), which are general stochastic algorithms inspired by the natural biological evolution and/or social behavior of species, can theoretically be used to solve any complex optimization problems including those found in SDNs. This paper reviewsfour types of EAs that are widely applied in current SDNs: Genetic algorithms(GAs), Particle Swarm Optimization(PSO), Ant Colony Optimization(ACO), and Simulated Annealing(SA) by discussing their techniques, summarizing their representative applications, and highlighting their issues and future works. To the best of our knowledge, our work is the first that compares the tech-niques and categorizes the applications of these four EAs in SDNs.
The evolutionary edit distance between two individuals in a population, i.e., the amount of applications of any genetic operator it would take the evolutionary process to generate one individual starting from the othe...
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ISBN:
(纸本)9781450349390
The evolutionary edit distance between two individuals in a population, i.e., the amount of applications of any genetic operator it would take the evolutionary process to generate one individual starting from the other, seems like a promising estimate for the diversity between said individuals. We introduce genealogical diversity, i.e., estimating two individuals' degree of relatedness by analyzing large, unused parts of their genome, as a computationally efficient method to approximate that measure for diversity.
A detailed review of a wide range of meta-heuristic and evolutionary algorithms in a systematic manner and how they relate to engineering optimization problems This book introduces the main metaheuristic algorithms an...
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ISBN:
(数字)9781119387053
ISBN:
(纸本)9781119386995
A detailed review of a wide range of meta-heuristic and evolutionary algorithms in a systematic manner and how they relate to engineering optimization problems This book introduces the main metaheuristic algorithms and their applications in optimization. It describes 20 leading meta-heuristic and evolutionary algorithms and presents discussions and assessments of their performance in solving optimization problems from several fields of engineering. The book features clear and concise principles and presents detailed descriptions of leading methods such as the pattern search (PS) algorithm, the genetic algorithm (GA), the simulated annealing (SA) algorithm, the Tabu search (TS) algorithm, the ant colony optimization (ACO), and the particle swarm optimization (PSO) technique. Chapter 1 of Meta-heuristic and evolutionary algorithms for Engineering Optimization provides an overview of optimization and defines it by presenting examples of optimization problems in different engineering domains. Chapter 2 presents an introduction to meta-heuristic and evolutionary algorithms and links them to engineering problems. Chapters 3 to 22 are each devoted to a separate algorithm— and they each start with a brief literature review of the development of the algorithm, and its applications to engineering problems. The principles, steps, and execution of the algorithms are described in detail, and a pseudo code of the algorithm is presented, which serves as a guideline for coding the algorithm to solve specific applications. This book: Introduces state-of-the-art metaheuristic algorithms and their applications to engineering optimization; Fills a gap in the current literature by compiling and explaining the various meta-heuristic and evolutionary algorithms in a clear and systematic manner; Provides a step-by-step presentation of each algorithm and guidelines for practical implementation and coding of algorithms; Discusses and assesses the performance of metaheuristic algorithms in mu
Wireless Sensor Networks (WSNs) are the key part of Internet of Things, as they provide the physical interface between on field information and backbone analytic engines. An important role of WSNs-when collecting vita...
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ISBN:
(纸本)9781740523905
Wireless Sensor Networks (WSNs) are the key part of Internet of Things, as they provide the physical interface between on field information and backbone analytic engines. An important role of WSNs-when collecting vital information-is to provide a consistent and reliable coverage. To Achieve this, WSNs must implement a highly reliable and efficient coverage recovery algorithm. In this paper, we take a fresh new approach to coverage recovery based on evolutionary algorithms. We propose EMACB-SA, which introduces a new evolutionary algorithm that selects coverage sets using a fitness function that balances energy efficiency and redundancy. The proposed algorithm improves network's coverage and lifetime in areas with heterogeneous event rate in comparison to previous works and hence, it is suitable for using in disaster management.
Histological images analysis is widely used to carry out diagnoses of different types of cancer. Digital image processing methods can be used for this purpose, leading to more objective diagnoses. Segmentation techniq...
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ISBN:
(纸本)9781538617106
Histological images analysis is widely used to carry out diagnoses of different types of cancer. Digital image processing methods can be used for this purpose, leading to more objective diagnoses. Segmentation techniques are applied to identify cellular structures indicative of diseases. In addition, the extracted features from these specific regions can aid pathologists in diagnoses decision using classification techniques. In this paper, we present an evaluation of evolutionary algorithms applied to lymphoma images for segmentation of their neoplastic cellular nuclei. In a second stage, we investigated the performance of the segmented images in the classification step. Initially, the R channel from RGB color model was processed with histogram equalization and Gaussian filter. In the segmentation step, optimization methods were analyzed in combination with the fuzzy 3-partition technique. Then, we also applied the valley-emphasis method and morphological operations to remove false positive regions in the post-processing step. Intensity and texture features were extracted and classified by the support vector machine method for diagnoses of 62 and 99 images of follicular lymphoma and mantle cell lymphoma, respectively. The results were evaluated through qualitative and quantitative analyses and the differential evolution method has reached the best results in the segmentation step. This technique allowed a relevant performance on the classification task with a mean value of accuracy of 99.38%.
How to achieve the balance between exploration and exploitation is a open problem in the field of evolutionary computation. Diversity is used to reflect the balance in practice. In this paper, a scheme that using colo...
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ISBN:
(纸本)9781509061822
How to achieve the balance between exploration and exploitation is a open problem in the field of evolutionary computation. Diversity is used to reflect the balance in practice. In this paper, a scheme that using colony fitness defined by us in selection is proposed to achieve the balance. Such a scheme can be widely used in different evolutionary algorithms. Our experiments are executed based on evolutionary algorithms based on different chromosome representation. Experimental results show that our scheme bring the improvement on diversity. Thus, solutions go significantly better in nine cases out of twenty-five ones, while go statistical worse in only one case.
Software testing is one of the primal phase in various software development lifecycle models and consumes approximately 70% of development time and 40% cost of the overall budget. Nowadays automated testing tools alon...
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ISBN:
(纸本)9781509056866
Software testing is one of the primal phase in various software development lifecycle models and consumes approximately 70% of development time and 40% cost of the overall budget. Nowadays automated testing tools along with different meta-heuristic algorithms which work similarly as simple testing techniques but they significantly outperforms when the complexity of the program is high are used in software testing phase to reduce the effort and time to test various program codes. Recent studies shows that various evolutionary algorithms (EA) like Artificial Immune System (AIS), Particle Swarm Optimization (PSO), Simulated annealing, Artificial Bee Colony (ABC), Cuckoo Search Algorithm (CSA), Ant colony optimization (ACO) are being functionalized in the field of Software Engineering to obtain optimal solutions. This review paper demonstrates the minimization of test cases using these evolutionary algorithms.
Empirical analysis of evolutionary algorithms (EAs) behavior is usually approached by computing relatively simple descriptive statistics like mean fitness and mean number of evaluations to convergence, or more theoret...
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ISBN:
(纸本)9781450349208
Empirical analysis of evolutionary algorithms (EAs) behavior is usually approached by computing relatively simple descriptive statistics like mean fitness and mean number of evaluations to convergence, or more theoretically sound statistical tests for finding significant differences between algorithms. However, these analyses do not consider situations where the EA failed to finish due to numerical errors or excessive computational time. Furthermore, the ability of an EA to continuously make search improvements is usually overlooked. In this paper we propose the use of the theory from survival analysis for empirically investigating the behavior of EAs, even in situations where not all the experiments finish in a reasonable time. We introduce two scenarios for the application of survival analysis in EAs. Survival trees, a machine learning technique adapted to the survival analysis scenario, are applied to automatically identify combinations of EA parameters with similar effect in the behavior of the algorithm.
The optimal sizing in water distribution networks (WDN) is of great interest because it allows the selection of alternative economical solutions that ensure design requirements at nodes (demands and pressure) and at l...
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The optimal sizing in water distribution networks (WDN) is of great interest because it allows the selection of alternative economical solutions that ensure design requirements at nodes (demands and pressure) and at lines (velocities). Among all the available design methodologies, this work analyzes those based on evolutionary algorithms (EAs). EAs are a combination of deterministic and random approaches, and the performance of the algorithm depends on the searching process. Each EA features specific parameters, and a proper calibration helps to reduce the randomness factor and improves the effectiveness of the search for minima. More specifically, the only common parameter to all techniques is the initial size of the random population (P). It is well known that population size should be large enough to guarantee the diversity of solutions and must grow with the number of decision variables. However, the larger the population size, the slower the convergence process. This work attempts to determine the population size that yields better solutions in less time. In order to get that, the work applies a method based on the concept of efficiency (E) of an algorithm. This efficiency relates the quality of the obtained solution with the computational effort that every EA requires to find the final design solution. This ratio E also represents an objective indicator to compare the performance of different algorithms applied to WDN optimization. The proposed methodology is applied to the pipe-sizing problem of three medium-sized benchmark networks, such as Hanoi, New York Tunnel and GoYang networks. Thus, from the currently available algorithms, this work includes evolutionary methodologies based on a Pseudo-Genetic Algorithm (PGA), Particle Swarm Optimization (PSO) and Harmony Search (HS). First, the different algorithm parameters for each network are calibrated. The values used for every EA are those that have been calculated in previous works. Secondly, specific paramete
In multi-label data, each instance belongs to a set of labels, instead of one label. Due to the increasing number of modern applications for multi-label data, multi-label classification has attracted the attention of ...
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ISBN:
(纸本)9781509043309
In multi-label data, each instance belongs to a set of labels, instead of one label. Due to the increasing number of modern applications for multi-label data, multi-label classification has attracted the attention of many researchers. Similar to single label data, eliminating irrelevant and/or redundant features plays an important role in improving the classifier performance. In this paper, meta-heuristic algorithms are employed to solve multi-label feature selection problem. Since the number of features in multi-label datasets is usually high, using these algorithms is not affordable in terms of computational complexity, and they may fail to find optimal feature subset. To solve this problem, irrelevant features are first removed using a filter method. Then, evolutionary algorithms are employed to find the most salient features. Experimental results demonstrate the efficiency of our proposed method compared to some existing multi-label features selection methods.
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