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.
15089682
Lagrange Multiplier Method for Multi-campaign Assignment Problem
It is crucial to maximize marketing efficiency and customer satisfaction in personalized marketing. In this paper, we raise the multiple recommendation problem which occurs when performing several personalized campaigns simultaneously. We formulate the multi-campaign assignment problem to solve this issue and propose methods for solving the problem. The most notable element is the Lagrange multiplier method. Roughly speaking, Lagrange multiplier reduces problem complexity with a minor impact on optimality. However, it is not easy to find Lagrange multipliers in exact accord with problem constraints. We use a genetic algorithm for finding optimal Lagrange multipliers. Through the experiments, we verify the effectiveness of the problem formulation and our genetic approach.
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.
6826338
Geometric crossover for multiway graph partitioning
Geometric crossover is a representation-independent generalization of the traditional crossover defined using the distance of the solution space. Using a distance tailored to the problem at hand, the formal definition of geometric crossover allows to design new problem-specific crossovers that embed problem-knowledge in the search. The standard encoding for multiway graph partitioning is highly redundant: each solution has a number of representations, one for each way of labeling the represented partition. Traditional crossover does not perform well on redundant encodings. We propose a new geometric crossover for graph partitioning based on a labeling-independent distance that filters the redundancy of the encoding. A correlation analysis of the fitness landscape based on this distance shows that it is well suited to graph partitioning. Our new genetic algorithm outperforms existing ones.
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.
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.
11689182
Decentralized Authentication Mechanisms for Object-based Storage Devices
Network-attached object-based storage separates data-path from control-path and enables direct interaction between clients and the storage devices. Clients interact with the file manager only to acquire the meta-data information and some cryptographic primitives, for example, access keys. Most of the current schemes rely on a centralized file manager to support these activities. This paper presents security mechanisms for decentralized authentication for object-based storage. The schemes are novel in several ways. First of all, they reduce the load on the file manager and free the system from central point of failure and denial of service attacks. We exploit Role-based Access Control (RBAC) to provide scalability and design authentication schemes that efficiently utilize RBAC. In most of the cases, the client needs to acquire only one access key from the file manager, which can be used by the client to further derive role-keys for the roles that he/she is permitted to play within an organization. Further, the number of cryptographic keys required for the purpose of authentication in these schemes is less as compared to the existing schemes. Finally, we also present two simple schemes that enable the clients to access objects stored on any device on the network using a single identity key.
36946020
Face Representation Method Using Pixel-to-Vertex Map (PVM) for 3D Model Based Face Recognition
3D model based approach for face recognition has been spotlighted as a robust solution under variant conditions of pose and illumination. Since a generative 3D face model consists of a large number of vertices, a 3D model based face recognition system is generally inefficient in computation time. In this paper, we propose a novel 3D face representation algorithm to reduce the number of vertices and optimize its computation time. Finally, we evaluate the performance of proposed algorithm with the Korean face database collected using a stereo-camera based 3D face capturing device.
1232413
Secure group services for storage area networks
Storage Area Networks, with their ability to offer high data availability, reliability and scalability, are a promising solution for the large scale storage needs of many enterprises. As with any distributed storage system, a major design challenge for a Storage Area Network (SAN) is to provide data integrity and confidentiality. In this paper we propose a solution which addresses these core security requirements. In particular, we focus on mechanisms that enable efficient key distribution to allow scalable data sharing. Our scheme uses strong cryptographic techniques to achieve data security and integrity. Further, we delegate the bulk of the cryptographic processing to the SAN entities (e.g., switches, routers or other network elements), thereby removing bottlenecks at the disks and causing minimal inconvenience to the hosts. By recognizing the peer nature of the group of SAN entities, we propose efficient group key mechanisms that do not involve any centralized key distribution servers. This allows our scheme to be scalable and be free from any single point of failure or attack.
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.
61949387
Quantizer design and distributed encoding algorithm for source localization in sensor networks
In this paper, we propose a quantizer design algorithm that is optimized for source localization in sensor networks. For this application, the goal is to minimize the amount of information that the sensor nodes have to exchange in order to achieve a certain source localization accuracy. We show that this goal can be achieved more efficiently when "application-specific" quantizers are used. Our proposed quantizer design algorithm uses a cost function that takes into account the distance between the actual source position and the position estimated based on quantized data. We also propose a distributed encoding algorithm that is applied after quantization and achieves rate savings by merging quantization bins without any degradation of localization performance. The merging technique in the encoding algorithm exploits the fact that certain combinations of quantization bins at each node cannot occur because the corresponding spatial regions have an empty intersection. We apply these algorithms to a system where an acoustic sensor model is employed for localization. For this case, we introduce the equally distance-divided quantizer (EDQ), designed so that quantizer partitions correspond to a uniform partitioning in terms of distance. Our simulations show the improved performance of our quantizer over traditional quantizer designs. In addition, they show rate savings (32.8%, 5 nodes, 4 bits per node) when our novel bin-merging algorithms are used. Our results also show that an optimized bit allocation leads to significant improvements in localization performance with respect to a bit allocation that uses the same number of bits for each node.