2299968
Multi-campaign Assignment Problem and Optimizing Lagrange Multipliers
Customer relationship management is crucial in acquiring and maintaining royal customers. To maximize revenue and customer satisfaction, companies try to provide personalized services for customers. A representative effort is one-to-one marketing. The fast development of Internet and mobile communication boosts up the market of one-to-one marketing. A personalized campaign targets the most attractive customers with respect to the subject of the campaign. So it is important to expect customer preferences for campaigns. Collaborative Filtering (CF) and various data mining techniques are used to expect customer preferences for campaigns. Especially, since CF is fast and simple, it is widely used for personalization in e-commerce. There have been a number of customer-preference estimation methods based on CF. As personalized campaigns are frequently performed, several campaigns often happen to run simultaneously. It is often the case that an attractive customer for a specific campaign tends to be attractive for other campaigns. If we perform separate campaigns without considering this problem, some customers may be bombarded by a considerable number of campaigns. We call this overlapped recommendation problem. The larger the number of recommendations for a customer, the lower the customer interest for campaigns. In the long run, the customer response for campaigns drops. It lowers the marketing efficiency as well as customer satisfaction. Unfortunately, traditional methods only focused on the effectiveness of a single campaign and did not consider the problem with respect to the overlapped recommendations. In this paper, we define the multi-campaign assignment problem (MCAP) considering the overlapped recommendation problem and propose a number of methods for the issue including a genetic approach. We also verify the effectiveness of the proposed methods with field data.
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.
12076
Multi-attractor gene reordering for graph bisection
We propose a new gene reordering scheme for the graph bisection problem. Our gene reordering starts with two or more vertices to capture the clustering structure of graphs effectively. We devised a chromosome repairing method for hybrid genetic search, which helps exploit clusters when combined with gene reordering. Experimental tests showed that the suggested reordering scheme significantly improves the performance of genetic algorithms over previous reordering methods.
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.
19448510
A Genetic Approach for Gene Selection on Microarray Expression Data
Microarrays allow simultaneous measurement of the expression levels of thousands of genes in cells under different physiological or disease states. Because the number of genes exceeds the number of samples, class prediction on microarray expression data leads to an extreme “curse of dimensionality” problem. A principal goal of these studies is to identify a subset of informative genes for class prediction to reduce the curse of dimensionality. We propose a novel genetic approach that selects a subset of predictive genes for classification on the basis of gene expression data. Our genetic algorithm maximizes correlation between genes and classes and minimizes intercorrelation among genes. We tested the genetic algorithm on leukemia data sets and obtained improved results over previous results.
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.
14647313
Energy-efficient disk replacement and file placement techniques for mobile systems with hard disks
Mobile systems have usually used hard disks as the secondary storage devices because of their high capacity per cost and high I/O throughput. However, their high power consumption is the main limiting factor for extending their adoptions in mobile systems. In this paper, we propose enhanced file placement techniques for mobile platforms with multiple smaller disks (instead of a single large disk). We investigate that how many smaller disks are necessary to obtain energy saving while maintaining the required performance using both a simplified energy model and a realistic trace-based simulator under the proposed multiple disk configurations. We also propose energy-efficient file placement techniques, which aggregate files with common attributes the same set of disks. By skewing I/O operations, the proposed techniques achieve additional energy saving. Experimental results show that the proposed techniques can reduce the energy consumption by up to 43% when eight 1" disks are used instead of a single 2.5" disk with an acceptable increase in the average response time.
12500430
Energy-efficient file placement techniques for heterogeneous mobile storage systems
While hard disk drives are the most common secondary storage devices, their high power consumption and low shock-resistance limit them as an ideal mobile storage solution. On the other hand, flash memory devices overcome the main problems of hard disk drives, but they are still more expensive in the cost per bit over hard disk drives and can only support a limited number of erase cycles. In this paper, we show that combining the merits of a hard disk and a flash memory device can produce an energy-efficient secondary storage solution for mobile platforms. We propose an energy-efficient file placement technique for such heterogeneous storage systems. The proposed technique adapts an existing data concentration technique by separating read and write I/O requests. Experimental results show that the proposed technique reduces the energy consumption by up to 74.5% when the combination of a 1.8" disk and a flash memory is used instead of a single 2.5" disk, at the cost of small increase in the average response time.
37111410
Performance Comparison Between Backpropagation, Neuro-Fuzzy Network, and SVM
In this study, we compare the performance of well-known neural networks, namely, back-propagation (BP) algorithm, Neuro-Fuzzy network and Support Vector Machine (SVM) using the standard three database sets: Wisconsin breast cancer, Iris and wine data. Since such database have been useful for evaluating performance of a group of machine learning algorithms, a series of experiments have been carried out for three algorithms using the cross validation method. Results suggest that SVM outperforms the others and the Neuro-Fuzzy network is better than the BP algorithm for this data set.
43170896
MCCA: A high-throughput MAC strategy for next-generation WLANs
Wireless LAN technology has been shown to he a revolutionary development during the last decade. Recently popularized IEEE 802.11a/g-based products can support up to 54 Mb/s physical layer rate and provide wireless access to the Internet. However, in order to deal robustly with the unreliable wireless nature, the 802.11 medium access control protocol has a relatively large overhead and hence, the throughput performance is much worse than the underlying physical layer rate. Moreover, along with many emerging applications and services over WLANs, such as voice over WLAN and audio/video streaming, the demand lor faster and higher- capacity WLANs has been growing recently. In this article, we propose a new medium access control protocol for the next-generation high-speed WLANs. The proposed medium access control, called multi-user polling controlled channel access, is composed of two components: multi-layer frame aggregation, which performs aggregation at both the medium access control and the physical layers; and multi-user polling, used to reduce the contention overhead and in turn, achieve higher network utilization. Multi-user polling controlled channel access is compared with the 802.11e-enhanced distributed channel access medium access control. Highly enhanced medium access control efficiency can be achieved by applying multi-user polling controlled channel access. We show the improved medium access control performance in terms of the aggregate throughput of non-QoS Hows with relevant QoS requirements.
35466192
Scheduling on parallel identical machines to minimize total tardiness
This paper focuses on the problem of scheduling n independent jobs on m identical parallel machines for the objective of minimizing total tardiness of the jobs. We develop dominance properties and lower bounds, and develop a branch and bound algorithm using these properties and lower bounds as well as upper bounds obtained from a heuristic algorithm. Computational experiments are performed on randomly generated test problems and results show that the algorithm solves problems with moderate sizes in a reasonable amount of computation time. � 2005 Elsevier B.V. All rights reserved.