344302
New topologies for genetic search space
We propose three distance measures for genetic search space. One is a distance measure in the population space that is useful for understanding the working mechanism of genetic algorithms. Another is a distance measure in the solution space for K-grouping problems. This can be used for normalization in crossover. The third is a level distance measure for genetic algorithms, which is useful for measuring problem difficulty with respect to genetic algorithms. We show that the proposed measures are metrics and the measures are efficiently computed.
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.
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.
36639292
Lock-Gain Based Graph Partitioning
We propose a new heuristic for the graph partitioning problem. Based on the traditional iterative improvement framework, the heuristic uses a new type of gain in selecting vertices to move between partitions. The new type of gain provides a good explanation for the performance difference of tie-breaking strategies in KL-based iterative improvement graph partitioning algorithms. The new heuristic performed excellently. Theoretical arguments supporting its efficacy are also provided. As the proposed heuristic is considered a good candidate for local optimization engines in metaheuristics, we combined it with a genetic algorithm as a sample case and obtained a surprising result that even the average results over 1,000 runs equalled the best known for most graphs.
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.
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.
206667073
Fault-diagnosis for a class of distributed control multistage interconnection networks
A class of (2 log/sub 2/ N-1)-stage interconnection networks is suitable for fault-tolerance with effective use of the redundant paths which provide multiple disjoint paths between each pair of input/output terminals. This paper focuses on a new fault-diagnosis method for a class of distributed control (2 log/sub 2/ N-1)-stage interconnection networks. To perform this fault-diagnosis method, the self-routing tags based on a given multistage interconnection network are generated for each phase. Six tests with self-routing tags can detect a faulty switching element and a link stuck fault in a (2 log/sub 2/ N-1)-stage interconnection network. Regarding the fault location problem, the fault-location procedure to use O(log/sub 2/ log 2N) tests for a distributed control network is proposed.
6422061
A new branch and bound algorithm for minimizing mean tardiness in two-machine flowshops
Abstract We consider the two-machine flowshop scheduling problem with the objective of minimizing mean tardiness. Presented are several properties that are used to calculate lower bounds on total tardiness of jobs for a given partial sequence and to identify sequences dominated by others. We develop a branch and bound algorithm using these bounds and dominance rule. This algorithm is compared with an existing algorithm on randomly-generated test problems.
95258
Exploring robustness in group key agreement
Secure group communication is crucial for building distributed applications that work in dynamic environments and communicate over unsecured networks (e.g. the Internet). Key agreement is a critical part of providing security services for group communication systems. Most of the current contributory key agreement protocols are not designed to tolerate failures and membership changes during execution. In particular, nested or cascaded group membership events (such as partitions) are not accommodated. We present the first robust contributory key agreement protocols, resilient to any sequence of events while preserving the group communication membership and ordering guarantees.
11890469
Modeling Dynamic Perceptual Attention in Complex Virtual Environments
An important characteristic of a virtual human is the ability to direct its perceptual attention to entities and areas in a virtual environment in a manner that appears believable and serves a functional purpose. In this paper, we describe a perceptual attention model that mediates top-down and bottom-up attention processes of virtual humans in order for the virtual human to efficiently select important information with limited sensory capability within complex virtual environments.
6168983
Privacy Preserving Nearest Neighbor Search
Data mining is frequently obstructed by privacy concerns. In many cases data is distributed, and bringing the data together in one place for analysis is not possible due to privacy laws (e.g. HIPAA) or policies. Privacy preserving data mining techniques have been developed to address this issue by providing mechanisms to mine the data while giving certain privacy guarantees. In this work we address the issue of privacy preserving nearest neighbor search, which forms the kernel of many data mining applications. To this end, we present a novel algorithm based on secure multiparty computation primitives to compute the nearest neighbors of records in horizontally distributed data. We show how this algorithm can be used in three important data mining algorithms, namely LOF outlier detection, SNN clustering, and kNN classification