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evolutionary techniques for Web caching

DISTRIBUTED AND PARALLEL DATABASES 2002年第1期11卷 93-116页

作者： Vakali, A Aristotle Univ Dept Informat Thessaloniki 54006 Greece

Web caching has been proposed as an effective solution to the problems of network traffic and congestion, Web objects access and Web load balancing. This paper presents a model for optimizing Web cache content by applying either a genetic algorithm or an evolutionary programming scheme for Web cache content replacement. Three policies are proposed for each of the genetic algorithm and the evolutionary programming techniques, in relation to objects staleness factors and retrieval rates. A simulation model is developed and long term trace-driven simulation is used to experiment on the proposed techniques. The results indicate that all evolutionary techniques are beneficial to the cache replacement, compared to the conventional replacement applied in most Web cache server. Under an appropriate objective function the genetic algorithm has been proven to be the best of all approaches with respect to cache hit and byte hit ratios.

关键词： World Wide Web caching cache replacement algorithms genetic algorithms evolutionary programming

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An evolutionary based wavelet network for real-time power dispatch

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ELECTRIC POWER COMPONENTS AND SYSTEMS 2002年第11期30卷 1151-1166页

作者： Wang, WM Huang, CM Kun Shan Univ Technol Dept Elect Engn Tainan Taiwan

This article presents an evolutionary based wavelet network (EBWN) technique for real-time power dispatch regarding both fuel cost and environmental impact of emissions. The wavelet networks, constracted by an evolutionary computing algorithm, are composed of a three-layer structure that contains the wavelet, weighting, and summing nodes, respectively. To make the computed outputs fit the historical ones, the parameters of translation and dilation in the wavelet nodes and the weighting factors in the weighting nodes are tuned. Taking the advantages of global search abilities of the evolutionary programming (EP) and the multi-resolution as well as localization natures of the wavelets, the evolutionary based wavelet networks can thus construct and identify the inherent characteristics of the nonlinear system. Once the networks are trained properly, they can be applied to the real-time power dispatch environment. The effectiveness of the proposed approach has been demonstrated by two examples, the IEEE 30-bus 6-generator system and the Taipower Company (TPC) real system. Comparisons of learning performances are made to the existing artificial neural networks (ANNs) method.

关键词： wavelet network evolutionary programming real-time power dispatch

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Improved fast evolutionary program for optimum design of power transformer

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COMPEL-THE INTERNATIONAL JOURNAL FOR COMPUTATION AND MATHEMATICS IN ELECTRICAL AND ELECTRONIC ENGINEERING 2006年第4期25卷 995-1006页

作者： Subramanian, S. Bhuvaneswari, R. Annamalai Univ Fac Engn & Technol Dept Elect Engn Annamalainagar 608002 Tamil Nadu India

Purpose - The power transformer is one of the most important pieces of equipment in a power system. The necessity for the optimum design of a power transformer arises because the design chosen should satisfy all the limitations and restrictions placed on it. This paper presents an improved fast evolutionary programming (IFEP) technique for the optimal design of a three-phase power transformer. Design/methodology/approach - The optimization of the transformer design problem is formulated as an NLP problem, expressing the objective and constraint functions in terms of the selected independent variables. Here the cost of the transformer is considered as the objective function and is the sum of material cost of stampings and copper windings, cost of cooling tube arrangements, cost of cooling medium, insulation cost and labour cost. A computer program is written from which the optimal design parameters are obtained. For optimization, the classical evolutionary programming (CEP) technique and its variant the IFEP technique are used and the results are compared. Findings - The application of CEP and IFEP for transformer design has been demonstrated on two test cases. It has been observed that this IFEP outperforms the CEP in obtaining the optimum design of transformers of smaller as well as larger ratings in terms of execution time, convergence rate, quality and success rate. Originality/value - The proposed method results in the economical design of a three-phase power transformer which can significantly reduce the cost of manufacturing transformers.

关键词： transformers evolutionary programming optimization techniques

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Discovering predictive ensembles for transfer learning and meta-learning

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MACHINE LEARNING 2018年第1期107卷 177-207页

作者： Kordik, Pavel Cerny, Jan Fryda, Tomas FIT CVUT Thakurova 9 Prague 6 Czech Republic

Recent meta-learning approaches are oriented towards algorithm selection, optimization or recommendation of existing algorithms. In this article we show how data-tailored algorithms can be constructed from building blocks on small data sub-samples. Building blocks, typically weak learners, are optimized and evolved into data-tailored hierarchical ensembles. Good-performing algorithms discovered by evolutionary algorithm can be reused on data sets of comparable complexity. Furthermore, these algorithms can be scaled up to model large data sets. We demonstrate how one particular template (simple ensemble of fast sigmoidal regression models) outperforms state-of-the-art approaches on the Airline data set. Evolved hierarchical ensembles can therefore be beneficial as algorithmic building blocks in meta-learning, including meta-learning at scale.

关键词： Meta-learning Ensemble learning evolutionary algorithms evolutionary programming Combining classifiers Regression models Model blending Automatic algorithm selection Map reduce

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Gradient-based adaptation of continuous dynamic model structures

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INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE 2016年第1期47卷 249-263页

作者： La Cava, William G. Danai, Kourosh Univ Massachusetts Dept Mech & Ind Engn Amherst MA 01003 USA

A gradient-based method of symbolic adaptation is introduced for a class of continuous dynamic models. The proposed model structure adaptation method starts with the first-principles model of the system and adapts its structure after adjusting its individual components in symbolic form. A key contribution of this work is its introduction of the model's parameter sensitivity as the measure of symbolic changes to the model. This measure, which is essential to defining the structural sensitivity of the model, not only accommodates algebraic evaluation of candidate models in lieu of more computationally expensive simulation-based evaluation, but also makes possible the implementation of gradient-based optimisation in symbolic adaptation. The proposed method is applied to models of several virtual and real-world systems that demonstrate its potential utility.

关键词： evolutionary programming dynamic modelling system identification symbolic regression gradient-based adaptation

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Application and comparison of metaheuristic techniques to generation expansion planning in the partially deregulated environment

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IET GENERATION TRANSMISSION & DISTRIBUTION 2007年第1期1卷 111-118页

作者： Kannan, S. Slochanal, S. Mary Raja Baskar, S. Murugan, P. Coll Engn Dept Elect & Elect Engn Madurai 625015 Tamil Nadu India Thiagarajar Coll Engn Dept Elect & Elect Engn Madurai 6250015 Tamil Nadu India Coll Engn Elect Comm Engn Dept Madurai Tamil Nadu India

The deregulation of the electric utility industry is an on-going process in many developing countries. A novel generation expansion planning (GEP) model is proposed that is suitable for developing countries such as India in a partially deregulated environment. In a partially deregulated/restructured environment, both utilities and independent power producers (IPPs) participate in the generation market. In this model, the utility purchases electric power from the IPPs and sells it to the consumer. The utility maximises its profit and ensures profits for all the participating IPPs. In addition, the utility checks under/over investment and considers system security. national security (fuel-mix ratio), social welfare and reliability simultaneously. The budget constraints of the utility are to be taken into consideration during the expansion plan. Metaheuristic techniques., such as genetic algorithms, differential evolution, evolutionary programming, evolutionary strategy, particle swarm optimisation, tabu search, simulated annealing, and the hybrid approach are used to solve the restructured GEP problem, and their performances are evaluated and validated against the dynamic programming (DP) method for a synthetic test system having five types of candidate plant for the utility and three types of candidate plant for IPP, with a 6 year planning horizon. The effectiveness of the proposed modifications and techniques is also addressed.

关键词： partially deregulated environment power system reliability tabu search reliability particle swarm optimisation search problems power system security evolutionary programming India Power system planning and layout Power system management, operation and economics differential evolution Optimisation techniques generation expansion planning model social welfare genetic algorithms system security dynamic programming evolutionary strategy independent power producers power generation planning fuel-mix ratio electricity supply industry deregulation national security heuristic programming electric utility industry deregulation simulated annealing evolutionary computation developing countries metaheuristic techniques budget constraints under/over investment

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An intelligent personal spider (agent) for dynamic Internet/Intranet searching

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DECISION SUPPORT SYSTEMS 1998年第1期23卷 41-58页

作者： Chen, HC Chung, YM Ramsey, M Yang, CC Univ Arizona Karl Eller Grad Sch Management MIS Dept Tucson AZ 85721 USA Univ Arizona ECE Dept Tucson AZ 85721 USA

As Internet services based on the World-Wide Web become more popular, information overload has become a pressing research problem. Difficulties with search on Internet will worsen as the amount of on-line information increases. A scalable approach to Internet search is critical to the success of Internet services and other current and future National Information Infrastructure (NII) applications. As part of the ongoing Illinois Digital Library Initiative project, this research proposes an intelligent personal spider (agent) approach to Internet searching. The approach, which is grounded on automatic textual analysis and general-purpose search algorithms, is expected to be an improvement over the current static and inefficient Internet searches. In this experiment, we implemented Internet personal spiders based on best first search and genetic algorithm techniques. These personal spiders can dynamically take a user's selected starting homepages and search for the most closely related homepages in the web, based on the links and keyword indexing. A plain, static CGI/HTML-based interface was developed earlier, followed by a recent enhancement of a graphical, dynamic Java-based interface. Preliminary evaluation results and two working prototypes (available for Web access) are presented. Although the examples and evaluations presented are mainly based on Internet applications, the applicability of the proposed techniques to the potentially more rewarding Intranet applications should be obvious. In particular, we believe the proposed agent design can be used to locate organization-wide information, to gather new, time-critical organizational information, and to support team-building and communication in Intranets. (C) 1998 Elsevier Science B.V. All rights reserved.

关键词： agents machine learning spider evolutionary programming information retrieval semantic retrieval Java World-Wide Web Internet Intranet

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An evolutionary algorithm with a history mechanism for tuning a chess evaluation function

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APPLIED SOFT COMPUTING 2013年第7期13卷 3234-3247页

作者： Vazquez-Fernandez, Eduardo Coello Coello, Carlos A. Sagols Troncoso, Feliu D. CINVESTAV IPN Dept Comp Sci Evolutionary Computat Grp Mexico City 07300 DF Mexico CINVESTAV IPN Dept Math Mexico City 07360 DF Mexico

Here, we propose an evolutionary algorithm (i.e., evolutionary programming) for tuning the weights of a chess engine. Most of the previous work in this area has normally adopted co-evolution (i.e., tournaments among virtual players) to decide which players will pass to the following generation, depending on the outcome of each game. In contrast, our proposed method uses evolution to decide which virtual players will pass to the next generation based on the number of positions solved from a number of chess grandmaster games. Using a search depth of 1-ply, our method can solve 40.78% of the positions evaluated from chess grandmaster games (this value is higher than the one reported in the previous related work). Additionally, our method is capable of solving 53.08% of the positions using a historical mechanism that keeps a record of the "good" virtual players found during the evolutionary process. Our proposal has also been able to increase the competition level of our search engine, when playing against the program Chessmaster (grandmaster edition). Our chess engine reached a rating of 2404 points for the best virtual player with supervised learning, and a rating of 2442 points for the best virtual player with unsupervised learning. Finally, it is also worth mentioning that our results indicate that the piece material values obtained by our approach are similar to the values known from chess theory. (C) 2013 Elsevier B.V. All rights reserved.

关键词： evolutionary algorithm evolutionary programming Chess engine Artificial intelligence Chess evaluation function

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Optimization of energy systems based on evolutionary and Social metaphors

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ENERGY 2008年第2期33卷 171-179页

作者： Dimopoulos, George G. Frangopoulos, Christos A. Natl Tech Univ Athens Sch Naval Architecture & Marine Engn Zografos 15773 Greece

Optimization problems that arise in energy systems design often have several features that hinder the use of many optimization techniques. These optimization problems have non-continuous mixed variable definition domains, are heavily constrained, are multimodal (i.e. have many local optima) and, foremost, the functions used to define the engineering optimization problem are often computationally intensive. Three methods are tested here: (a) a Struggle Genetic Algorithm (StrGA), (b) a Particle Swarm Optimization Algorithm (PSOA), and (c) a PSOA with Struggle Selection (PSOStr). The last is a hybrid of the evolutionary StrGA and the socially inspired PSOA. They are tested in four purely mathematical and three energy systems thermoeconomic optimization problems. All of the methods solved successfully all the problems. The PSOStr, however, outperformed the other methods in terms of both solution accuracy and computational Cost (i.e. function evaluations). (C) 2007 Elsevier Ltd. All rights reserved.

关键词： energy systems particle swarm optimization evolutionary programming

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A comparison of memetic algorithms for the spread spectrum radar polyphase codes design problem

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2008年第8期21卷 1233-1238页

作者： Perez-Bellido, Angel M. Salcedo-Sanz, Sancho Ortiz-Garcia, Emilio G. Portilla-Figueras, Jose A. Lopez-Ferreras, Francisco Univ Alcala Dept Signal Theory & Commun Madrid Spain

This paper presents three memetic algorithms to solve the spread spectrum radar polyphase code design problem, based on evolutionary programming, Particle Swarm Optimization and Differential Evolution, respectively. These global search heuristics are hybridized with a gradient-based local search procedure which includes a dynamic step adaptation procedure to perform accurate and efficient local search for better solutions. We have compared the different memetic algorithms proposed in several numerical examples, and we have also demonstrated the performance of our approaches against existing approaches for this problem. (C) 2008 Elsevier Ltd. All rights reserved,

关键词： Polyphase codes Code design evolutionary programming Memetic algorithms

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