In June 2008, the Eclipse open platform released a new dependency management system called p2. That system was based on the translation of the dependency management problem into a pseudo-Boolean optimizationproblem, ...
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In June 2008, the Eclipse open platform released a new dependency management system called p2. That system was based on the translation of the dependency management problem into a pseudo-Boolean optimizationproblem, to be handled by the Sat4j solver. Since then, p2 has been more tightly integrated with Sat4j, the platform opened a public plugin repository (the Eclipse marketplace) which relies on p2 to install the available plugins and their dependencies, and became the favorite way to install plugins in the Eclipse community. This paper summarizes the issues raised by Eclipse dependency management, its pseudo-Boolean encoding within p2, its extension for Linux package management with p2cudf, and concludes with lessons learned on using research software in production systems.
Genetic algorithm has made lots of achievements in the aspect of solving constrained optimizationproblems, but engineering design problem is one of typical optimizationproblems for complicated constraint condition a...
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Genetic algorithm has made lots of achievements in the aspect of solving constrained optimizationproblems, but engineering design problem is one of typical optimizationproblems for complicated constraint condition and correlative variable parameters. The results optimized by classical mathematical optimization method are often poor. In this paper, one hybrid search strategy was designed aiming to the defects of simple genetic algorithm. With improvement, the algorithm is less likely to trap in local optimum. And the simulation test shows that the algorithm for engineering design problem has made great effects in stability and convergence precision.
Efficient use of the network's resources to collect information about objects (events) in a given volume of interest (VOI) is a key challenge in large-scale sensor networks. Multi-sensor multi-target tracking in s...
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Efficient use of the network's resources to collect information about objects (events) in a given volume of interest (VOI) is a key challenge in large-scale sensor networks. Multi-sensor multi-target tracking in surveillance applications is an example where the network's success in tracking targets, efficiently and effectively, hinges significantly on the network's ability to allocate the right set of sensors to the right set of targets so as to achieve optimal performance which minimizes the number of uncovered targets. This task can be even more complicated when both the sensors and the targets are mobile. To ensure timely tracking of mobile targets, the surveillance sensor network needs to perform the following tasks in real-time: (i) target-to-sensor allocation;(ii) sensor mobility control and coordination. The computational complexity of these two tasks presents a challenge, particularly in large scale dynamic network applications. This paper proposes a formulation based on the Semi-flocking algorithm and the distributed constraint optimization problem (DCOP). The semi-flocking algorithm performs multi-target motion control and coordination, a DCOP modeling algorithm performs the target engagement task. As will be demonstrated experimentally in the paper, this algorithmic combination provides an effective approach to the multisensor/multi-target engagement problem, delivering optimal target coverage as well as maximum sensors utilization.
Distributed target allocation and tracking is an important research problem. This problem is complex but has many applications in various domains, including, pervasive computing, surveillance and military systems. In ...
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Distributed target allocation and tracking is an important research problem. This problem is complex but has many applications in various domains, including, pervasive computing, surveillance and military systems. In this paper we propose a technique to solve the target to sensor allocation problem by modeling the problem as a hierarchical Distributed constraint optimization problem (HDCOP). Distributed Constrain optimizationproblems (DCOPs) tend to be computationally expensive and often intractable, particularly in large problem spaces such as Wireless Sensor Networks (WSNs). To address this challenge we propose changing the sensor to target allocation as a hierarchical set of smaller DCOPs with a shared system of constraints. Thus, we avoid significant computational and communication costs. Furthermore, in contrast to other DCOP modeling methods, a non-binary variable modeling is employed to reduce the number of intra-agent constraints. To evaluate the performance of the proposed approach, we use the surveillance system of the Regional Waterloo Airport as a test case. Two DCOP solution algorithms are considered, namely, the Distributed Breakout Algorithm (DBA) and the Asynchronous Distributed optimization (ADOPT). We evaluate the computational and communication costs of these two algorithms for solving the target to sensor allocation problem using the proposed hierarchical formulation. We compare the performance of these algorithms with respect to the incurred computational and communication costs. (C) 2013 Elsevier B.V. All rights reserved.
Background: In a single proteomic project, tandem mass spectrometers can produce hundreds of millions of tandem mass spectra. However, majority of tandem mass spectra are of poor quality, it wastes time to search them...
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Background: In a single proteomic project, tandem mass spectrometers can produce hundreds of millions of tandem mass spectra. However, majority of tandem mass spectra are of poor quality, it wastes time to search them for peptides. Therefore, the quality assessment (before database search) is very useful in the pipeline of protein identification via tandem mass spectra, especially on the reduction of searching time and the decrease of false identifications. Most existing methods for quality assessment are supervised machine learning methods based on a number of features which describe the quality of tandem mass spectra. These methods need the training datasets with knowing the quality of all spectra, which are usually unavailable for the new datasets. Results: This study proposes an unsupervised machine learning method for quality assessment of tandem mass spectra without any training dataset. This proposed method estimates the conditional probabilities of spectra being high quality from the quality assessments based on individual features. The probabilities are estimated through a constraint optimization problem. An efficient algorithm is developed to solve the constraint optimization problem and is proved to be convergent. Experimental results on two datasets illustrate that if we search only tandem spectra with the high quality determined by the proposed method, we can save about 56% and 62% of database searching time while losing only a small amount of high-quality spectra. Conclusions: Results indicate that the proposed method has a good performance for the quality assessment of tandem mass spectra and the way we estimate the conditional probabilities is effective.
constraints occur in many application areas of interest to evolutionary computation. The area considered here is Bayesian networks (BNs), which is a probability-based method for representing and reasoning with uncerta...
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constraints occur in many application areas of interest to evolutionary computation. The area considered here is Bayesian networks (BNs), which is a probability-based method for representing and reasoning with uncertain knowledge. This work deals with constraints in BNs and investigates how tournament selection can be adapted to better process such constraints in the context of abductive inference. Abductive inference in BNs consists of finding the most probable explanation given some evidence. Since exact abductive inference is NP-hard, several approximate approaches to this inference task have been developed. One of them applies evolutionary techniques in order to find optimal or close-to-optimal explanations. A problem with the traditional evolutionary approach is this: As the number of constraints determined by the zeros in the conditional probability tables grows, performance deteriorates because the number of explanations whose probability is greater than zero decreases. To minimize this problem, this paper presents and analyzes a new evolutionary approach to abductive inference in BNs. By considering abductive inference as a constraint optimization problem, the novel approach improves performance dramatically when a BN's conditional probability tables contain a significant number of zeros. Experimental results are presented comparing the performances of the traditional evolutionary approach and the approach introduced in this work. The results show that the new approach significantly outperforms the traditional one.
This paper propose a novel learning approach that applies NeuroEvolution of Augmenting Topology (NEAT) based learning algorithm to resolve Call Admission Control (CAC) combined with resource allocation in adaptive mul...
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ISBN:
(纸本)9781424448203
This paper propose a novel learning approach that applies NeuroEvolution of Augmenting Topology (NEAT) based learning algorithm to resolve Call Admission Control (CAC) combined with resource allocation in adaptive multimedia wireless networks;this not only decides whether to accept or reject a request call, but also determines the allocated bandwidth to that requesting call. The objective is to maximize the network revenue and maintain predefined QoS constraints. The QoS constraints are classified as two categories: long period constraints and instantaneous constraints. Long period constraints are handled by a constraint handling method called Superiority of Feasible Points. Instantaneous constraints and system limitations are handled by an External Supervisor.
This paper propose a novel learning approach that applies NeuroEvolution of Augmenting Topology (NEAT) based learning algorithm to resolve Call Admission Control(CAC) combined with resource allocation in adaptive mult...
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This paper propose a novel learning approach that applies NeuroEvolution of Augmenting Topology (NEAT) based learning algorithm to resolve Call Admission Control(CAC) combined with resource allocation in adaptive multimedia wireless networks;this not only decides whether to accept or reject a request call,but also determines the allocated bandwidth to that requesting *** objective is to maximize the network revenue and maintain predefined QoS *** QoS constraints are classified as two categories:long period constraints and instantaneous *** period constraints are handled by a constraint handling method called Superiority of Feasible *** constraints and system limitations are handled by an External Supervisor.
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