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Recommending diverse friends in signed social networks based on adaptive soft consensus paradigm using variable length genetic algorithm

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WORLD WIDE WEB-INTERNET AND WEB INFORMATION SYSTEMS 2018年第5期21卷 1285-1321页

作者： Agarwal, Vinti Bharadwaj, K. K. Jawaharlal Nehru Univ Sch Comp & Syst Sci New Delhi 110067 India

Despite the strategic role played by individuals, who act as intermediaries between distinct groups of people, the problem of recommending diverse friends in signed social networks (SSNs) still remains largely unexplored. Our model integrates homophily and diversity to develop an adaptive consensus based framework, which involves fuzzy group decision making analysis by leveraging on the signed social links and underlying users' preferences, to offer lists of connections which are diverse as well as relevant. Our contributions are three-fold. First, we modeled the fuzzy binary adjacency relations between users, thereafter referred as decision makers (DMs), exploiting users' preferences conferred on a set of items, and then higher order fuzzy m-ary adjacency relations are constructed to represent the grade of agreement between a set of m DMs. Further, in order to evaluate the relevance of each decision maker involved in the decision making process, we introduce a novel diversity measure based on the knowledge of socio-psychological theories and the information contained in social and interest links. Next, by employing variable-length genetic algorithm, an idea of adaptive consensus is explored to evolve groups of experts which are highly consensual as well as influential in the social network. Finally, on the basis of opinions gleaned from the members of these groups, sign of unknown links are predicted, thereby generating a top-N recommendations list of diverse friends. Extensive experimental study conducted on Epinions dataset illustrates that our proposed scheme outperforms the traditional graph-based methods.

关键词： Signed social networks Diversity Trust Social balance theory Social status variable-length genetic algorithm Fuzzy adjacency relations Consensus evaluation

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Agent-based modeling and evolutionary computation for disseminating public advisories about hazardous material emergencies

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COMPUTERS ENVIRONMENT AND URBAN SYSTEMS 2016年 57卷 12-25页

作者： Shafiee, M. Ehsan Berglund, Emily Zechman N Carolina State Univ Dept Civil Construct & Environm Engn 2501 Stinson Dr208 Mann HallCampus Box 7908 Raleigh NC 27695 USA

In the event of a large-scale disaster, an important aspect of humanitarian logistics is the distribution of information or warnings to the affected population. This research develops the problem formulation and solution approach for a specific routing for relief problem, in which warnings should be disseminated to an affected community, using public announcement systems mounted on emergency vehicles. The problem statement is formulated to maximize the number of individuals of a community who are protected. An evolutionary algorithm framework is developed by coupling an agent-based model with a variable-length genetic algorithm to route emergency vehicles. The dynamics of interactions among consumers, emergency vehicles, and the spatiotemporal trajectory of the hazard are simulated using an agent-based modeling approach, and a variable-length genetic algorithm approach selects routes to warn a maximum number of consumers before they are affected by the emergency. The example that is explored in this research is contamination of a water distribution network. A fleet of emergency vehicles is equipped with public address systems and is deployed to warn consumers to stop using contaminated water. The framework is demonstrated for an illustrative virtual city, Mesopolis. The results of the evolutionary algorithm framework are compared with two conventional routing optimization approaches, including a covering tour problem approach and a manual routing approach, for four contamination scenarios. The evolutionary algorithm can be applied to route emergency service vehicles to broadcast information for other emergencies, such as flash flooding, hazardous materials incidents, and severe weather. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： variable-length genetic algorithm Agent-based model Water distribution network Water contamination events Vehicle routing for relief problem Covering tour problem Complex adaptive system Route alert

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Wireless Sensor Network Deployment Using Stochastic Optimization Techniques - A Comparative Study

Wireless Sensor Network Deployment Using Stochastic Optimiza...

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International Conference on Computing and Network Communications (CoCoNet)

作者： Deif, Dina Gadallah, Yasser Amer Univ Cairo Dept Elect & Commun Engn Cairo Egypt

ISBN: (纸本)9781467373098

For over a decade, the Minimum Cost Coverage Deployment Problem (MCCDP) has been an active research topic in the field of Wireless Sensor Networks (WSNs). The MCCDP is modeled as a combinatorial constrained optimization problem which was proven to be NP-Complete. Consequently, many of the existing deployment algorithms designed to solve the MCCDP are based on stochastic optimization techniques. These techniques can be heuristic such as Greedy Heuristics (GHs) or metaheuristic such as genetic algorithms (GAs) and Ant Colony Optimization (ACO). However, the performance of these algorithms has not been evaluated thoroughly, especially for solving MCCDPs of large scales, i.e. for deploying large-scale WSNs. Moreover, quantitative comparisons of the different algorithms that belong to these techniques are yet to be conducted, to the best of our knowledge. These comparisons are quite important in providing some insights into the suitability of a technique for a specific WSN deployment problem at hand. In this paper, we present a comparative performance evaluation of four of the existing algorithms designed to solve the MCCDP which are based on the aforementioned stochastic optimization techniques. Performance is evaluated in terms of quality of obtained results, computational cost, convergence speed and deployment scalability. Based on the statistical analysis of the experimental results, a comparison is conducted to highlight the comparative strengths and shortcomings of each algorithm.

关键词： Wireless sensor networks sensor node deployment topology construction heuristic optimization metaheuristic optimization greedy heuristic fixed-length genetic algorithm variable-length genetic algorithm ant colony optimization performance evaluation

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Comparison of Scoring Methods for Interactive Evolutionary Computation based Image Retouching System 12

Comparison of Scoring Methods for Interactive Evolutionary C...

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14th International Conference on genetic and Evolutionary Computation Conference (GECCO)

作者： Yoon, Du-Mim Kim, Kyung-Joong Sejong Univ Dept Comp Engn Seoul South Korea

ISBN: (纸本)9781450311786

Interactive evolutionary computation (IEC) is a useful tool to automate human's creative process such as image drawing, music composition, and so on. In this paper, we apply the IEC to generate a new image from the original one using a set of filters. In professional image editing software, there provides us with a lot of filters and sometimes the sequential use of several filters produces interesting results. Usually, searching for the useful combination of filters is a kind of trial-and-error tasks. Or, only experts can define a good combination of filters from their long experience. In this work, our system incrementally adds or deletes filters based on user's evaluation on the results of image retouching. Recently, people take photos using their smartphones and it is not a trivial to give a number of consecutive evaluations to guide the IEC search. Based on the observation, we try to compare several scoring methods for the IEC to get the best results for the image retouching system. They are a simple binary scoring (Good & Bad), discrete multiple choices (Five Star), and real-valued scoring (Slider). Experimental results on five human subjects show that the Good & Bad is appropriate for the retouching system.

关键词： Interactive genetic algorithm Scoring method Photo retouching variable-length genetic algorithm Subject test Personal photo Image filters GIMP

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Design of neuro-fuzzy systems using a hybrid evolutionary learning algorithm

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JOURNAL OF INFORMATION SCIENCE AND ENGINEERING 2007年第2期23卷 463-477页

作者： Lin, Cheng-Jian Xu, Yong-Ji Chaoyang Univ Technol Dept Comp Sci & Informat Engn Taichung 413 Taiwan

In this paper, a TSK-type fuzzy model (TFM) with a hybrid evolutionary learning algorithm (HELA) is proposed. The proposed HELA method combines the compact genetic algorithm (CGA) and the modified variable-length genetic algorithm (MVGA). Both the number of fuzzy rules and the adjustable parameters in the TFM are designed concurrently using the HELA method. In the proposed HELA method, individuals of the same length constitute the same group, with multiple groups in a population. Moreover, the proposed HELA method adopts the compact genetic algorithm (CGA) to carry out the elite-based reproduction strategy. The CGA represents a population as a probability distribution over the set of solutions and is operationally equivalent to the order-one behavior of the simple GA. The evolution processes of a population consist of three major operations: group reproduction using the compact genetic algorithm, variable two-part individual crossover, and variable two-part mutation. Computer simulations have demonstrated that the proposed HELA method performs better than some existing methods.

关键词： evolutionary algorithms TSK-type fuzzy model variable-length genetic algorithm identification control

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A hybrid evolutionary learning algorithm for TSK-type fuzzy model design

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MATHEMATICAL AND COMPUTER MODELLING 2006年第5-6期43卷 563-581页

作者： Lin, CH Xu, YJ Chaoyang Univ Technol Dept Comp Sci & Informat Engn Taichung 41349 Taiwan

In this paper, a TSK-type fuzzy model (TFM) with a hybrid evolutionary learning algorithm (HELA) is proposed. The proposed HELA method combines the compact genetic algorithm (CGA) and the modified variable-length genetic algorithm (MVGA). Both the number of fuzzy rules and the adjustable parameters in the TFM are designed concurrently by the HELA method. In the proposed HELA method, individuals of the same length constitute the same group, and there are multiple groups in a population. Moreover, the proposed HELA adopts the compact genetic algorithm (CGA) to carry out the elite-based reproduction strategy. The CGA represents a population as a probability distribution over the set of solutions and is operationally equivalent to the order-one behavior of the simple GA. The evolution processes of a population consist of three major operations: group reproduction using the compact genetic algorithm, variable two-part individual crossover, and variable two-part mutation. Computer simulations have demonstrated that the proposed HELA method gives a better performance than some existing methods. (c) 2005 Elsevier Ltd. All rights reserved.

关键词： evolutionary algorithms TSK-type fuzzy model variable-length genetic algorithm identification control

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