3094791
Multicampaign assignment problem
It is crucial to maximize targeting efficiency and customer satisfaction in personalized marketing. State-of-the-art techniques for targeting focus on the optimization of individual campaigns. Our motivation is the belief that the effectiveness of a campaign with respect to a customer is affected by how many precedent campaigns have been recently delivered to the customer. We raise the multiple recommendation problem, which occurs when performing several personalized campaigns simultaneously. We formulate the multicampaign assignment problem to solve this issue and propose algorithms for the problem. The algorithms include dynamic programming and efficient heuristic methods. We verify by experiments the effectiveness of the problem formulation and the proposed algorithms.
27587473
Generalized cycle crossover for graph partitioning
We propose a new crossover that generalizes cycle crossover to permutations with repetitions and naturally suits partition problems. We tested it on graph partitioning problems obtaining excellent results.
17681470
New Usage of Sammon's Mapping for Genetic Visualization
It is a hard problem to understand the fitness landscape of a problem as well as the evolution of genetic algorithms. For the purpose, we adopt Sammon's mapping for the investigation. We demonstrate its usefulness by applying it to the graph partitioning problem which is a well-known NP-hard problem. Also, through the investigation of schema traces, we explain the genetic process and the reordering effect in the genetic algorithm.
7564297
Problem-Independent Schema Synthesis for Genetic Algorithms
As a preprocessing for genetic algorithms, static reordering helps genetic algorithms effectively create and preserve high-quality schemata, and consequently improves the performance of genetic algorithms. In this paper, we propose a static reordering method independent of problem-specific knowledge. One of the novel features of our reordering method is that it is applicable to any problem with no information about the problem. The proposed method constructs a weighted complete graph from the gene distances calculated from solutions with relatively high fitnesses, transforms them into a gene-interaction graph, and finds a gene rearrangement. Extensive experimental results showed significant improvement for a number of applications.
15690106
Genetic Search For Fixed Channel Assignment Problem With Limited Bandwidth
We propose a hybrid genetic algorithm for the fixed channel assignment problem with limited bandwidth. A local optimization algorithm was devised to enhance the genetic algorithm's fine-tunning. In a matrix representation, the local optimization algorithm improves on a chromosome by horizontal and vertical moves of channels. The crossover is directly performed on two-dimensional matrices. Experimental results showed dramatic improvement against previous works.
10542004
Investigation of the Fitness Landscapes and Multi-parent Crossover for Graph Bipartitioning
An empirical study is performed on the local-optimum space of graph bipartitioning. We examine some statistical features of the fitness landscape. They include the cost-distance correlations and the properties around the central area of local optima. The study revealed some new notable results about the properties of the fitness landscape; e.g., the central area yielded fairly good quality in the local-optimum space. We performed an experiment on a spectrum of different exploitation strengths of the central areas. From the results, it seems attractive to exploit the central area, but excessive or insufficient exploitation is not desirable.
205099619
Simulated annealing and genetic algorithms for scheduling products with multi-level product structure
We consider a short-term production scheduling problem in a manufacturing system producing products with multi-level product structure, and their components and subassemblies. The problem is to schedule production of components, subassemblies, and final products with the objective of minimizing the weighted sum of tardiness and earliness of the items. We consider due dates of final products, precedence relationships among items, and processing capacity of the manufacturing system. The scheduling problem is solved with simulated annealing and genetic algorithms, which are search heuristics often used for global optimization in a complex search space. To compare the performance of these algorithms with the finite loading method, which is often used in practice, computational experiments are carried out using randomly generated test problems and results are reported.
1907049
A spatial mapping algorithm for heterogeneous coarse-grained reconfigurable architectures
In this work, we investigate the problem of automatically mapping applications onto a coarse-grained reconfigurable architecture and propose an efficient algorithm to solve the problem. We formalize the mapping problem and show that it is NP-complete. To solve the problem within a reasonable amount of time, we divide it into three subproblems: covering, partitioning and layout. Our empirical results demonstrate that our technique produces nearly as good performance as hand-optimized outputs for many kernels
11050110
Communication-Efficient Group Key Agreement
Traditionally, research in secure group key agreement focuses on minimizing the computational overhead for cryptographic operations, and minimizing the communication overhead and the number of protocol rounds is of secondary concern. The dramatic increased in computation power that we witnessed during the past years exposed network delay in WANs as the primary culprit for a negative performance impact on key agreement protocols. The majority of previously proposed protocols optimize the cryptographic overhead of the protocol. However, high WAN delay negatively impacts their efficiency. The goal of this work is to construct a new protocol that trades off computation with communication efficiency. We resurrect a key agreement protocol previously proposed by Steer et al. We extend it to handle dynamic groups and network failures such as network partitions and merges. The resulting protocol suite is provably secure against passive adversaries and provides key independence, i.e. a passive adversary who knows any proper subset of group keys cannot discover any other group keys not included in the subset. Furthermore, the protocol is simple, fault-tolerant, and well suited for high-delay wide area network.
12493055
A methodology for evaluation of boundary detection algorithms on medical images
Image segmentation is the partition of an image into a set of nonoverlapping regions whose union is the entire image. The image is decomposed into meaningful parts which are uniform with respect to certain characteristics, such as gray level or texture. In this paper, we propose a methodology for evaluating medical image segmentation algorithms wherein the only information available is boundaries outlined by multiple expert observers. In this case, the results of the segmentation algorithm can be evaluated against the multiple observers' outlines. We have derived statistics to enable us to find whether the computer-generated boundaries agree with the observers' hand-outlined boundaries as much as the different observers agree with each other. We illustrate the use of this methodology by evaluating image segmentation algorithms on two different applications in ultrasound imaging. In the first application, we attempt to find the epicardial and endocardial boundaries from cardiac ultrasound images, and in the second application, our goal is to find the fetal skull and abdomen boundaries from prenatal ultrasound images.
9480518
Feedback capacity of the first-order moving average Gaussian channel
Despite numerous bounds and partial results, the feedback capacity of the stationary nonwhite Gaussian additive noise channel has been open, even for the simplest cases such as the first-order autoregressive Gaussian channel studied by Butman, Tiernan and Schalkwijk, Wolfowitz, Ozarow, and more recently, Yang, Kavccaronicacute, and Tatikonda. Here we consider another simple special case of the stationary first-order moving average additive Gaussian noise channel and find the feedback capacity in closed form. Specifically, the channel is given by Yi=Xi+Zi, i=1,2,..., where the input {X i} satisfies a power constraint and the noise {Zi} is a first-order moving average Gaussian process defined by Zi=alphaUi-1+Ui, |alpha|les 1, with white Gaussian innovations Ui, i=0,1,.... We show that the feedback capacity of this channel is CFB=-log x0  where x0 is the unique positive root of the equation rhox2=(1-x2)(1-|alpha|x)2 and rho is the ratio of the average input power per transmission to the variance of the noise innovation Ui. The optimal coding scheme parallels the simple linear signaling scheme by Schalkwijk and Kailath for the additive white Gaussian noise channel-the transmitter sends a real-valued information-bearing signal at the beginning of communication and subsequently refines the receiver's knowledge by processing the feedback noise signal through a linear stationary first-order autoregressive filter. The resulting error probability of the maximum likelihood decoding decays doubly exponentially in the duration of the communication. Refreshingly, this feedback capacity of the first-order moving average Gaussian channel is very similar in form to the best known achievable rate for the first-order autoregressive Gaussian noise channel given by Butman