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Data Flows Mapping in Fog Computing Infrastructures Using evolutionary Inspired Heuristic 1

9th International Conference on Cloud Computing and Services Science (CLOSER)

作者： Canali, Claudia Lancellotti, Riccardo Univ Modena & Reggio Emilia Dept Engn Enzo Ferrari Modena Italy

ISBN: (数字)9783030494322

ISBN: (纸本)9783030494315;9783030494322

The need for scalable and low-latency architectures that can process large amount of data from geographically distributed sensors and smart devices is a main driver for the popularity of the fog computing paradigm. A typical scenario to explain the fog success is a smart city where monitoring applications collect and process a huge amount of data from a plethora of sensing devices located in streets and buildings. The classical cloud paradigm may provide poor scalability as the amount of data transferred risks the congestion on the data center links, while the high latency, due to the distance of the data center from the sensors, may create problems to latency critical applications (such as the support for autonomous driving). A fog node can act as an intermediary in the sensor-to-cloud communications where pre-processing may be used to reduce the amount of data transferred to the cloud data center and to perform latency-sensitive operations. In this book chapter we address the problem of mapping sensors over the fog nodes with a twofold contribution. First, we introduce a formal model for the mapping model that aims to minimize response time considering both network latency and processing time. Second, we present an evolutionary-inspired heuristic (using Genetic Algorithms) for a fast and accurate resolution of this problem. A thorough experimental evaluation, based on a realistic scenario, provides an insight on the nature of the problem, confirms the viability of the GAs to solve the problem, and evaluates the sensitivity of such heuristic with respect to its main parameters.

关键词： Fog computing Optimization model evolutionary programming Genetic algorithms Smart cities

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A probabilistic chart parser implemented with an evolutionary algorithm

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5th International Conference on Intelligent Text Processing and Computational Linguistics

作者： Araujo, L Univ Complutense Madrid Dept Sistemas Informat & Programac E-28040 Madrid Spain

ISBN: (纸本)3540210067

Classic parsing methods use complete search techniques to find the different interpretations of a sentence. However, the size of the search space increases exponentially with the length of the sentence or text to be parsed and the size of the grammar, so that exhaustive search methods can fail to reach a solution in a reasonable time. Nevertheless, large problems can be solved approximately by some kind of stochastic techniques, which do not guarantee the optimum value, but allow adjusting the probability of error by increasing the number of points explored. evolutionary Algorithms are among such techniques. This paper presents a. stochastic chart parser based on an evolutionary algorithm which works with a population of partial parsings. The paper describes the relationships between the elements of a classic chart parser and those of the evolutionary algorithm. The model has been implemented, and the results obtained for texts extracted from the Susanne corpus are presented.

关键词： evolutionary programming partial parsing probabilistic grammars

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A new binary-coded approach to the unit commitment problem using Grey wolf optimization 28

A new binary-coded approach to the unit commitment problem u...

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28th Iranian Conference on Electrical Engineering (ICEE)

作者： Majidi, H. Emadaleslami, M. Haghifam, M. R. Tarbiat Modares Univ Fac Elect & Comp Engn Tehran Iran

ISBN: (纸本)9781728172965

One of the most difficult problems in the management of economically power systems is the unit commitment problem. The mathematical complexity of the problem increases with the increase in unit numbers. Given the high dimensions of this problem, meta-heuristic methods can act as one of the best ways of solving this problem. one of the challenges that meta-heuristic strategies faces is the low number of feasible answers compared to non-feasible ones while trying to satisfy the unit commitment constraints which can cause problems in the convergence of these methods. A new approach based on binary encoding in the population was proposed in this paper, using the Gray Wolf Optimization Algorithm (GWO), to solve the complexity of the unit commitment problem. The GWO is then applied to 10 thermal test units. Comprehensive numerical tests are carried out to check the GWO's efficacy using the approach proposed. The simulation results are presented and compared to numerous current heuristic methods that show the GWO's superior performance using the method proposed to solve the UC problem.

关键词： unit commitment (UC) grey wolf optimization (GWO) evolutionary programming constrained optimization binary-coded

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EMOCS: evolutionary Multi-objective Optimisation for Clinical Scorecard Generation

EMOCS: Evolutionary Multi-objective Optimisation for Clinica...

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Genetic and evolutionary Computation Conference (GECCO)

作者： Fraser, Diane P. Keedwell, Edward Michell, Stephen L. Sheridan, Ray Univ Exeter Exeter Devon England RD&E Hosp Exeter Devon England

ISBN: (纸本)9781450361118

Clinical scorecards of risk factors associated with disease severity or mortality outcome are used by clinicians to make treatment decisions and optimize resources. This study develops an automated tool or framework based on evolutionary algorithms for the derivation of scorecards from clinical data. The techniques employed are based on the NSGA-II Multi-objective Optimization Genetic Algorithm (GA) which optimizes the Pareto-front of two clinically-relevant scorecard objectives, size and accuracy. Three automated methods are presented which improve on previous manually derived scorecards. The first is a hybrid algorithm which uses the GA for feature selection and a decision tree for scorecard generation. In the second, the GA generates the full scorecard. The third is an extended full scoring system in which the GA also generates the scorecard scores. In this system combinations of features and thresholds for each scorecard point are selected by the algorithm and the evolutionary process is used to discover near-optimal Pareto-fronts of scorecards for exploration by expert decision makers. This is shown to produce scorecards that improve upon a human derived example for C. Difficile, an important infection found globally in communities and hospitals, although the methods described are applicable to any disease where the required data is available.

关键词： Multi-objective optimisation evolutionary programming Medicine Prediction/forecasting

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Research on a Model of Order Allocation for Virtual Enterprises

Research on a Model of Order Allocation for Virtual Enterpri...

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Global Conference on Digital Design and Manufacturing Technology

作者： Wang, H. F. Zhejiang Business Technol Inst Sch Continuing Educ Ningbo 315012 Peoples R China

ISBN: (纸本)9783037850718

Industrial clusters can be found very often in the world, particularly in many developing countries. To build virtual enterprise based on an industrial cluster is one of the most important ways to improve the agility and competitiveness of manufacturing enterprises in the cluster. One of the key factors towards the success of virtual enterprises is the correct selection of cooperative partners in the virtual enterprise. An approach of order allocation and partner selection in the environment of industrial clusters is proposed. This approach is composed of two stages: task-resource matching and quantitative evaluation. In the first stage the potential candidates are identified and in the second stage evolutionary programming is applied to deal with partner selection and order allocation problem. The target function, in which the load rate of candidate enterprise is taken as the main variable, is developed, and a simplified example is used to verify the feasibility of the proposed approach. The result suggests that the proposed model and the algorithm obtain satisfactory solutions.

关键词： Order Allocatio Virtual Enterprise Industrial Cluster evolutionary programming

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On-line Economic Dispatch of Distributed Generation Using Artificial Neural Networks 5th

On-line Economic Dispatch of Distributed Generation Using Ar...

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5th International Conference on Swarm, evolutionary, and Memetic Computing (SEMCCO)

作者： Babu, M. Arumuga Mahalakshmi, R. Kannan, S. Karuppasamypandiyan, M. Bhuvanesh, A. Tejaa Shakthi Inst Technol Women Dept EEE Coimbatore Tamil Nadu India Sri Krishna Coll Technol Dept EEE Coimbatore Tamil Nadu India Ramco Inst Technol Dept EEE Rajapalayam India Univ Coll Engn Dept EEE Tiruchirappalli India Mepco Schlenk Engn Coll Dept EEE Sivakasi India

ISBN: (纸本)9783319202945;9783319202938

In recent years, distributed generators (DG) are most widely installed in distribution system to meet the increasing demand and especially to reduce the losses. According to demand, dispatch of generator should be modified for economic operation. The Economic Dispatch (ED) of DGs are usually solved by conventional methods such as Lambda iteration method, Dynamic programming etc., or any optimization technique such as Genetic algorithm (GA), evolutionary programming (EP) etc., This off-line methods of solving ED problem require comparatively large computation time and are not suitable for on-line applications. Therefore, it is important to estimate Real Power dispatch values within a short period. This paper presents an On-line ED of various non-renewable DGs for various demands using Artificial Neural Networks namely Back Propagation Neural Network (BPNN) and Radial Basis Function Neural Network (RBFNN). The input pattern for Neural Networks (NN) is demand and output is corresponding optimal real power dispatch. The input and output patterns for NN is obtained using evolutionary programming method. In this work two diesel engines and two fuel cells are used as DG. This case study has been illustrated in a distribution system having two types of four numbers of DGs. The test result shows that the proposed method is better for real time ED.

关键词： Back propagation neural network Distributed generators Economic dispatch evolutionary programming Radial basis function neural network

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Evolving Four-Part Harmony Using Genetic Algorithms 1

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Conference on EvoApplications 2011: EvoCOMPLEX, EvoGAMES, EvoIASP, EvoINTELLIGENCE, EvoNUM, AND EvoSTOC

作者： Donnelly, Patrick Sheppard, John Montana State Univ Dept Comp Sci Bozeman MT 59715 USA

ISBN: (数字)9783642205200

ISBN: (纸本)9783642205194

This paper presents a genetic algorithm that evolves a four-part musical composition melodically, harmonically, and rhythmically. Unlike similar attempts in the literature, our composition evolves from a single musical chord without human intervention or initial musical material. The mutation rules and fitness evaluation are based on common rules from music theory. The genetic operators and individual mutation rules are selected from probability distributions that evolve alongside the musical material.

关键词： Genetic Algorithm evolutionary programming Melody Harmony Rhythm Music Composition

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evolutionary approach for solving economic dispatch in power system

Evolutionary approach for solving economic dispatch in power...

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National Power Engineering Conference (PECon 2003)

作者： Rahimullah, BNS Ramlan, EI Rahman, TKA Univ Teknol Mara Fac Elect Engn Shah Alam 40450 Selangor Malaysia

ISBN: (纸本)0780382080

The problem of economic dispatch has been forwarded and solved by numerous methods. This paper provides alternative methods to solve the problem. In this paper, evolutionary programming (EP) Is used as one of the techniques to solve the problem of economic dispatch in power system. Log-normal Gaussian mutation or commonly known as meta-EP, is used as the essential operator of generating the sufficient power in order to fulfil demand at a minimum cost. The proposed EP method provides a solution consisting suitable power generated of each generator and meeting the demand with minimum total cost. The study also investigates the differences of using standard EP against meta-EP to solve the same problem. The comparisons between the both methods and GA solution to solve the problems are also highlighted ill this paper. The study findings show that both EP methods perform better compared to GA in solving the economic dispatch problem. However, meta-EP seems to be more robust in solving problems in a bigger search space compared to the original EP. The study conducted for the comparison is based on the solution and performance of each algorithm in solving the problem.

关键词： economic dispatch evolutionary programming

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Optimal allocation and sizing of embedded generators

Optimal allocation and sizing of embedded generators

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National Power and Energy Conference (PECon 2004)

作者： Rahman, TKA Rahim, SRA Musirin, I Univ Teknol MARA Fac Elect Engn Shah Alam 40450 Selangor Malaysia

ISBN: (纸本)0780387244

Recent years, the number of embedded generators installed in distribution system has been increasing in many parts of the world. Depending on their operating characteristics and locations, embedded generators could significantly affect the voltage profile, network losses and fault level in a distribution system. This paper presents a new technique for determining optimal allocation and sizing of embedded generator in a distribution system. Sensitivity indices based on voltage stability improvement with respect to change in injected active and reactive power at a load bus were derived and used to identify the suitable location for the embedded generators. In order to determine the optimal output of the embedded generators, an evolutionary programming optimization technique was developed with an objective to minimize the distribution losses while satisfying the voltage constraint in the system. The proposed technique was tested on the 69 bus distribution system and the results shown a significant reduction in distribution losses and voltage profile improvement in the system with the implementation of the embedded generation at the suitable location and optimal sizing.

关键词： embedded generation loss minimization sensitivity analysis evolutionary programming

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Optimal construction of montages from mathematical functions on a spectrum of order-disorder preference

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JOURNAL OF MATHEMATICS AND THE ARTS 2022年第4期16卷 347-373页

作者： Smith-Miles, Kate Munoz, Mario Andres Univ Melbourne Sch Math & Stat Parkville Vic 3010 Australia

We previously generated diverse mathematical functions that are difficult for optimization algorithms. Represented as 2D contour plots, each image depicts a 'blue river' running through an intricate landscape. This paper describes the challenge of constructing an aesthetic montage of these images. A survey revealed a spectrum of tastes, divergent in preference from order to disorder, considering the structure created by connecting these 'blue rivers'. A new artwork, Negentropy Triptych, was created to depict this spectrum by manually swapping images from a random arrangement, guided by human eye to enhance or destroy the structure. An optimization algorithm automates the process, with the results of its efforts to emulate the artistic vision presented and discussed. The challenges faced by the algorithm, despite exploring several objective functions, highlight the difficulties of capturing the goals that a human decision-maker can easily achieve. Therefore, machine learning of these goals is a promising future direction. [GRAPHICS]

关键词： evolutionary programming optimization problems exploratory landscape analysis genetic algorithms evolved mathematical functions aesthetics mosaic arrangement global structure

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