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.
Among many other sub-types, one sub-type of ad hoc network is Vehicular ad hoc Network (VANET). VANET can be further categorized in sub-domains like Vehicle to Infrastructure (V2I), Vehicle to Vehicle (V2V), Vehicle t...
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ISBN:
(纸本)9781509064359
Among many other sub-types, one sub-type of ad hoc network is Vehicular ad hoc Network (VANET). VANET can be further categorized in sub-domains like Vehicle to Infrastructure (V2I), Vehicle to Vehicle (V2V), Vehicle to pedestrian or other equipment (V2X) and Hybrid (V2V+V2I+V2X). V2V communication is the primary focus of this paper. Different methodologies are available in the literature for optimization of V2V communication. Clustering is one of them, in clustering vehicles, the same vicinity are grouped together for efficient communication. Different evolutionary algorithms for clustering already have been implemented to route information among nodes. Two evolutionary algorithms are applied for optimizing communication among the vehicles and the clustering problem in the VANETs. The bio inspired evolutionary algorithms are Comprehensive Learning Particle Swarm Optimization (CLPSO) and Multi-Objective Particle Swarm Optimization (MOPSO). After implementation, comparison for the mentioned algorithms is used to depict the results. The experimental results show that CLPSO is providing better results than MOPSO.
Noise cancellation is one of the important signal processing functions of any communication system, as noise affects data integrity. In existing systems, traditional filters are used to cancel the noise from the recei...
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ISBN:
(纸本)9781509042289
Noise cancellation is one of the important signal processing functions of any communication system, as noise affects data integrity. In existing systems, traditional filters are used to cancel the noise from the received signals. These filters use fixed hardware which is capable of filtering specific frequency or a range of frequencies. However, next generation communication technologies, such as cognitive radio, will require the use of adaptive filters that can dynamically reconfigure their filtering parameters for any frequency. To this end, a few noise cancellation techniques have been proposed, including least mean squares (LMS) and its variants. However, these algorithms are susceptible to non-linear noise and fail to locate the global optimum solution for de-noising. In this paper, we investigate the efficiency of two global search optimization based algorithms, genetic algorithm and particle swarm optimization in performing noise cancellation in cognitive radio systems. These algorithms are implemented and their performances are compared to that of LMS using bit error rate and mean square error as performance evaluation metrics. Simulations are performed with additive white Gaussian noise and random nonlinear noise. Results indicate that GA and PSO perform better than LMS for the case of AWGN corrupted signal but for non-linear random noise PSO outperforms the other two algorithms.
One of the main reasons for the success of evolutionary algorithms (EAs) is their general-purposeness, i.e. the fact that they can be applied in a straight forward manner to a broad range of optimization problems, wit...
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ISBN:
(纸本)9783319558493;9783319558486
One of the main reasons for the success of evolutionary algorithms (EAs) is their general-purposeness, i.e. the fact that they can be applied in a straight forward manner to a broad range of optimization problems, without any specific prior knowledge. On the other hand, it has been shown that incorporating a priori knowledge, such as expert knowledge or empirical findings, can significantly improve the performance of an EA. However, integrating knowledge in EAs poses numerous challenges. It is often the case that the features of the search space are unknown, hence any knowledge associated with the search space properties can be hardly used. In addition, a priori knowledge is typically problem-specific and hard to generalize. In this paper, we propose a framework, called Knowledge Integrated evolutionary Algorithm (KIEA), which facilitates the integration of existing knowledge into EAs. Notably, the KIEA framework is EA-agnostic, i.e. it works with any evolutionary algorithm, problem-independent, i.e. it is not dedicated to a specific type of problems and expandable, i.e. its knowledge base can grow over time. Furthermore, the framework integrates knowledge while the EA is running, thus optimizing the consumption of computational power. In the preliminary experiments shown here, we observe that the KIEA framework produces in the worst case an 80% improvement on the converge time, w.r.t. the corresponding "knowledge-free" EA counterpart.
This paper is mainly intended to compare image fusion method using different evolutionary algorithms and a comparison between these methods. The survey focuses on region-based fusion techniques, which is a major area ...
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ISBN:
(纸本)9781509045594
This paper is mainly intended to compare image fusion method using different evolutionary algorithms and a comparison between these methods. The survey focuses on region-based fusion techniques, which is a major area of research. The paper compares image fusion processes using various evolutionary algorithms and illustrates the advantages and disadvantages of these algorithms. This survey illustrates that a method of image fusion can also be included in the DE optimization stage with the block size optimization. Finally, it is concluded that spatial frequency can be used as the sharpness criterion and evolutionary algorithms perform better in block size optimization.
In this study we use survey expectations about a wide range of economic variables to forecast real activity. We propose an empirical approach to derive mathematical functional forms that link survey expectations to ec...
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In this study we use survey expectations about a wide range of economic variables to forecast real activity. We propose an empirical approach to derive mathematical functional forms that link survey expectations to economic growth. Combining symbolic regression with genetic programming we generate two survey-based indicators: a perceptions index, using agents' assessments about the present, and an expectations index with their expectations about the future. In order to find the optimal combination of both indexes that best replicates the evolution of economic activity in each country we use a portfolio management procedure known as index tracking. By means of a generalized reduced gradient algorithm we derive the relative weights of both indexes. In most economies, the survey-based predictions generated with the composite indicator outperform the benchmark model for one-quarter ahead forecasts, although these improvements are only significant in Austria, Belgium and Portugal.
During the last decades, energy consumption has become a topic of interest for algorithm designers, particularly when devoted to networked devices and mainly when handheld ones are involved. Moreover energy consumptio...
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ISBN:
(纸本)9783319653402;9783319653396
During the last decades, energy consumption has become a topic of interest for algorithm designers, particularly when devoted to networked devices and mainly when handheld ones are involved. Moreover energy consumption has become a matter of paramount importance in nowadays environmentally conscious society. Although a number of studies are already available, not many have focused on evolutionary algorithms (EAs). Moreover, no previous attempt has been performed for modeling energy consumption behavior of EAs considering different hardware platforms. This paper thus aims at not only analyzing the influence of the main EA parameters in their energy related behavior, but also tries for the first time to develop a model that allows researchers to know how the algorithm will behave in a number of hardware devices. We focus on a specific member of the EA family, namely Genetic Programming (GP), and consider several devices when employed as the underlying hardware platform. We apply a Fuzzy Rules Based System to build the model that allows then to predict energy required to find a solution, given a previously chosen hardware device and a set of parameters for the algorithm.
In this paper, a target region based multiobjective evolutionary algorithm framework is proposed to incorporate preference into the optimization process. It aims at finding a more fine-grained resolution of a target r...
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ISBN:
(纸本)9781509046010
In this paper, a target region based multiobjective evolutionary algorithm framework is proposed to incorporate preference into the optimization process. It aims at finding a more fine-grained resolution of a target region without exploring the whole set of Pareto optimal solutions. It can guide the search towards the regions on the Pareto Front which are of real interest to the decision maker. The algorithm framework has been combined with SMS-EMOA, R2-EMOA, NSGA-II to form three target region based multiobjective evolutionary algorithms: T-SMS-EMOA, T-R2-EMOA and T-NSGA-II. In these algorithms, three ranking criteria are applied to achieve a well-converged and well-distributed set of Pareto optimal solutions in the target region. The three criteria are: 1. Non-dominated sorting;2. indicators (hypervolume or R2 indicator) or crowding distance in the new coordinate space (i.e. target region) after coordinate transformation;3. the Chebyshev distance to the target region. Rectangular and spherical target regions have been tested on some benchmark problems, including continuous problems and discrete problems. Experimental results show that new algorithms can handle the preference information very well and find an adequate set of Pareto-optimal solutions in the preferred regions quickly. Moreover, the proposed algorithms have been enhanced to support multiple target regions and preference information based on a target point or multiple target points. Some results of enhanced algorithms are presented.
Several speed-up techniques for evolutionary algorithms (EA) are considered in this paper. Our long-term research is oriented towards development of highly accelerated EA for solving large, non-linear, constrained opt...
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ISBN:
(纸本)9781538627266
Several speed-up techniques for evolutionary algorithms (EA) are considered in this paper. Our long-term research is oriented towards development of highly accelerated EA for solving large, non-linear, constrained optimization problems. In particular, we briefly discuss here advances in development and samples of numerical analysis for already preliminarily proposed speed-up techniques, including smoothing and balancing, adaptive step-by-step mesh refinement, as well as a'posteriori error analysis and related techniques. Important engineering applications in computational mechanics are planned, including residual stress analysis in railroad rails, and vehicle wheels, as well as a wide class of problems resulting from the Physically Based Approximation of experimental and/or numerical data. The improved EA provides significant speed-up of convergence and/or possibility of solving such large problems, when the standard EA fails.
This paper concerns evolutionary algorithms for minimization exclusive-or sum-of-products representations of Boolean functions. These representations are used in logic synthesis for certain class of circuits. Minimiza...
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ISBN:
(纸本)9781538618103
This paper concerns evolutionary algorithms for minimization exclusive-or sum-of-products representations of Boolean functions. These representations are used in logic synthesis for certain class of circuits. Minimization is based on a decomposition for Boolean functions with parameter function. Selection of this function is a search task which can be solved with evolutionary algorithms. algorithms for obtaining approximately minimal formulas for Boolean functions of up to 8 variables are proposed.
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