The choice of the data type representation has significant impacts on the resource utilisation, maximum clock frequency and power consumption of any hardware design. Although arithmetic hardware units for the fixed-po...
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The choice of the data type representation has significant impacts on the resource utilisation, maximum clock frequency and power consumption of any hardware design. Although arithmetic hardware units for the fixed-point format can improve performance and reduce energy consumption, the process of tuning the right bit length is known as a time-consuming task, since it is a combinatorial optimisation problem guided by the accumulative arithmetic computation error. A novel evolutionary approach to accelerate the process of converting algorithms from the floating-point to fixed-point format is presented. Results are demonstrated by converting three computing-intensive algorithms from the mobile robotic scenario, where data error accumulated during execution is influenced by external factors, such as sensor noise and navigation environment characteristics. The proposed evolutionary algorithm accelerated the conversion process by up to 2.5 x against the state-of-the-art methods, allowing even further bit-length optimisations.
Mobile applications require dynamic reconfiguration services (DRS) to self-adapt their behavior to the context changes (e.g., scarcity of resources). Dynamic Software Product Lines (DSPL) are a well-accepted approach ...
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Mobile applications require dynamic reconfiguration services (DRS) to self-adapt their behavior to the context changes (e.g., scarcity of resources). Dynamic Software Product Lines (DSPL) are a well-accepted approach to manage runtime variability, by means of late binding the variation points at runtime. During the system's execution, the DRS deploys different configurations to satisfy the changing requirements according to a multiobjective criterion (e.g., insufficient battery level, requested quality of service). Search-based software engineering and, in particular, multiobjective evolutionary algorithms (MOEAs), can generate valid configurations of a DSPL at runtime. Several approaches use MOEAs to generate optimum configurations of a Software Product Line, but none of them consider DSPLs for mobile devices. In this paper, we explore the use of MOEAs to generate at runtime optimum configurations of the DSPL according to different criteria. The optimization problem is formalized in terms of a Feature Model (FM), a variability model. We evaluate six existing MOEAs by applying them to 12 different FMs, optimizing three different objectives (usability, battery consumption and memory footprint). The results are discussed according to the particular requirements of a DRS for mobile applications, showing that PAES and NSGA-II are the most suitable algorithms for mobile environments. (C) 2015 Elsevier Inc. All rights reserved.
In this paper a methodology for the delineation of local labour markets (LLMs) using evolutionary algorithms is proposed. This procedure, based on that in Florez-Revuelta et al. [13,14], introduces three modifications...
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In this paper a methodology for the delineation of local labour markets (LLMs) using evolutionary algorithms is proposed. This procedure, based on that in Florez-Revuelta et al. [13,14], introduces three modifications. First, initial groups of municipalities with a minimum size requirement are built using the travel time between them. Second, a not fully random initiation algorithm is proposed. And third, as a final stage of the procedure, a contiguity step is implemented. These modifications significantly decrease the computational times of the algorithm (up to a 99%) without any deterioration of the quality of the solutions. The optimization algorithm may give a set of potential solutions with very similar values with respect to the objective function what would lead to different partitions, both in terms of number of markets and their composition. In order to capture their common aspects an algorithm based on a cluster partitioning of k-means type is presented. This stage of the procedure also provides a ranking of LLMs foci useful for planners and administrations in decision-making processes on issues related to labour activities. Finally, to evaluate the performance of the algorithm a toy example with artificial data is analysed. The full methodology is illustrated through a real commuting data set of the region of Aragon (Spain).
In the paper several types of evolutionary algorithms have been tested regarding the dynamic nonlinear multivariable system model. We have defined three problems regarding the observed system: the first is the so-call...
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In the paper several types of evolutionary algorithms have been tested regarding the dynamic nonlinear multivariable system model. We have defined three problems regarding the observed system: the first is the so-called grey box identification where we search for the characteristic of the system's valve, the second problem is black box identification where we search the model of the system with the usage of system's measurements and the third one is a system's controller design. We solved these problems with the usage of genetic algorithms, differential evolution, evolutionary strategies, genetic programming and a developed approach called AMEBA algorithm. All methods have been proven to be very useful for solving problems of the grey box identification and design of the controller for the mentioned system but AMEBA algorithm have also been successfully used in black box identification problem where it generated a suitable model.
evolutionary algorithms are powerful search techniques which have been used successfully in many different domains. Parallel evolutionary algorithm has become a research focus due to its easy implement and promise sub...
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evolutionary algorithms are powerful search techniques which have been used successfully in many different domains. Parallel evolutionary algorithm has become a research focus due to its easy implement and promise substantial gains in performance. In this paper a framework of tree-modelbased parallel evolutionary algorithm (T-PEA) is proposed. The presented method employs Bayesian Dirichlet metric to construct a tree model from a set of potential solutions, which is then used to model potential solutions and guide exploration in the search space. The correctness and rationality of the proposed method for learning tree models are analyzed and proved in the context of genetic and evolutionary. The method is important not only for T-PEA, but also for machine learning and data mining. The experimental results show that the proposed algorithm can efficiently and rapidly converge and obtain the optimal solution from all test functions.
Parameter control methods for metaheuristics with reinforcement learning put forward so far usually present the following shortcomings: (1) Their training processes are usually highly time-consuming and they are not a...
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Parameter control methods for metaheuristics with reinforcement learning put forward so far usually present the following shortcomings: (1) Their training processes are usually highly time-consuming and they are not able to benefit from parallel or distributed platforms;(2) they are usually sensitive to their hyperparameters, which means that the quality of the final results is heavily dependent on their values;(3) and limited benchmarks have been used to assess their generality. This paper addresses these issues by proposing a methodology for training out-of-the-box parameter control policies for mono-objective non-niching evolutionary and swarm-based algorithms using distributed reinforcement learning with population-based training. The proposed methodology is suitable to be used in any mono-objective optimization problem and for any mono-objective and non-niching evolutionary and swarm-based algorithm. The results in this paper achieved through extensive experiments show that the proposed method satisfactorily improves all the aforementioned issues, overcoming constant, random and human-designed policies in several different scenarios.
Regression test suites are necessary to ensure that changes to the system made after bug fixes or reimplementation have not corrupted the intended functionality. However, because of the complexity of current hardware ...
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ISBN:
(纸本)9781479967803
Regression test suites are necessary to ensure that changes to the system made after bug fixes or reimplementation have not corrupted the intended functionality. However, because of the complexity of current hardware systems, it is desirable to have optimized regression suites that provide the highest verification coverage with minimal simulation time and resources. In this paper, we introduce a coverage-directed optimization algorithm for creating optimized regression suites from verification stimuli that were evaluated in simulation-based verification environment. The results of our experiments show that the size of the final regression suites are significantly improved in comparison to the original test suit. For our experimental system, we were able to eliminate 94.4% redundant stimuli from the original test suite while retaining the same level of coverage.
In this paper, we propose an alternative novel method based on the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to solve the problem of ranking and comparing algorithms. In evolutionary comp...
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In this paper, we propose an alternative novel method based on the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to solve the problem of ranking and comparing algorithms. In evolutionary computation, algorithms are executed several times and then a statistic in terms of mean values and standard deviations are calculated. In order to compare algorithms performance it is very common to handle such issue by means of statistical tests. Ranking algorithms, e.g., by means of Friedman test may also present limitations since they consider only the mean value and not the standard deviation of the results. Since the TOPSIS is not able to handle directly this kind of data, we develop an approach based on TOPSIS for algorithm ranking named as A-TOPSIS. In this case, the alternatives consist of the algorithms and the criteria are the benchmarks. The rating of the alternatives with respect to the criteria are expressed by means of a decision matrix in terms of mean values and standard deviations. A case study is used to illustrate the method for evolutionary algorithms. The simulation results show the feasibility of the A-TOPSIS to find out the ranking of algorithms under evaluation. (C) 2015 Published by Elsevier B.V.
The paper handles the issue of business dynamics in a service industry. Authors have used web semantics for service industry and analysed its optimum solution using evolutionary algorithm and set theory concept.
ISBN:
(纸本)9780986041945
The paper handles the issue of business dynamics in a service industry. Authors have used web semantics for service industry and analysed its optimum solution using evolutionary algorithm and set theory concept.
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